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Posted by Santiago Balseiro, Staff Research Scientist, Google Research, and Associate Professor at Columbia University, and Vahab Mirrokni, Distinguished Scientist, Google Research

The emergence of digital technologies has transformed decision making across commercial sectors such as airlines, online retailing, and internet advertising. Today, real-time decisions need to be repeatedly made in highly uncertain and rapidly changing environments. Moreover, organizations usually have limited resources, which need to be efficiently allocated across decisions. Such problems are referred to as online allocation problems with resource constraints, and applications abound. Some examples include:

• Bidding with Budget Constraints: Advertisers increasingly purchase ad slots using auction-based marketplaces such as search engines and ad exchanges. A typical advertiser can participate in a large number of auctions in a given month. Because the supply in these marketplaces is uncertain, advertisers set budgets to control their total spend. Therefore, advertisers need to determine how to optimally place bids while limiting total spend and maximizing conversions.
• Dynamic Ad Allocation: Publishers can monetize their websites by signing deals with advertisers guaranteeing a number of impressions or by auctioning off slots in the open market. To make this choice, publishers need to trade off, in real-time, the short-term revenue from selling slots in the open market and the long-term benefits of delivering good quality spots to reservation ads.
• Airline Revenue Management: Planes have a limited number of seats that need to be filled up as much as possible before a flight’s departure. But demand for flights changes over time and airlines would like to sell airline tickets to the customers who are willing to pay the most. Thus, airlines have increasingly adopted sophisticated automated systems to manage the pricing and availability of airline tickets.
• Personalized Retailing with Limited Inventories: Online retailers can use real-time data to personalize their offerings to customers who visit their store. Because product inventory is limited and cannot be easily replenished, retailers need to dynamically decide which products to offer and at what price to maximize their revenue while satisfying their inventory constraints.

The common feature of these problems is the presence of resource constraints (budgets, contractual obligations, seats, or inventory, respectively in the examples above) and the need to make dynamic decisions in environments with uncertainty. Resource constraints are challenging because they link decisions across time — e.g., in the bidding problem, bidding too high early can leave advertisers with no budget, and thus missed opportunities later. Conversely, bidding too conservatively can result in a low number of conversions or clicks.

Two central resource allocation problems faced by advertisers and publishers in internet advertising markets.

In this post, we discuss state-of-the-art algorithms that can help maximize goals in dynamic, resource-constrained environments. In particular, we have recently developed a new class of algorithms for online allocation problems, called dual mirror descent, that are simple, robust, and flexible. Our papers have appeared in Operations Research, ICML’20, and ICML’21, and we have ongoing work to continue progress in this space. Compared to existing approaches, dual mirror descent is faster as it does not require solving auxiliary optimization problems, is more flexible because it can handle many applications across different sectors with minimal modifications, and is more robust as it enjoys remarkable performance under different environments.

Online Allocation Problems
In an online allocation problem, a decision maker has a limited amount of total resources (B) and receives a certain number of requests over time (T). At any point in time (t), the decision maker receives a reward function (f_t) and resource consumption function (b_t), and takes an action (x_t). The reward and resource consumption functions change over time and the objective is to maximize the total reward within the resource constraints. If all the requests were known in advance, then an optimal allocation could be obtained by solving an offline optimization problem for how to maximize the reward function over time within the resource constraints¹.

The optimal offline allocation cannot be implemented in practice because it requires knowing future requests. However, this is still useful for framing the goal of online allocation problems: to design an algorithm whose performance is as close to optimal as possible without knowing future requests.

Achieving the Best of Many Worlds with Dual Mirror Descent
A simple, yet powerful idea to handle resource constraints is introducing “prices” for the resources, which enables accounting for the opportunity cost of consuming resources when making decisions. For example, selling a seat on a plane today means it can’t be sold tomorrow. These prices are useful as an internal accounting system of the algorithm. They serve the purpose of coordinating decisions at different moments in time and allow decomposing a complex problem with resource constraints into simpler subproblems: one per time period with no resource constraints. For example, in a bidding problem, the prices capture an advertiser’s opportunity cost of consuming one unit of budget and allow the advertiser to handle each auction as an independent bidding problem.

This reframes the online allocation problem as a problem of pricing resources to enable optimal decision making. The key innovation of our algorithm is using machine learning to predict optimal prices in an online fashion: we choose prices dynamically using mirror descent, a popular optimization algorithm for training machine learning predictive models. Because prices for resources are referred to as “dual variables” in the field of optimization, we call the resulting algorithm dual mirror descent.

The algorithm works sequentially by assuming uniform resource consumption over time is optimal and updating the dual variables after each action. It starts at a moment in time (t) by taking an action (x_t) that maximizes the reward minus the opportunity cost of consuming resources (shown in the top gray box below). The action (e.g., how much to bid or which ad to show) is implemented if there are enough resources available. Then, the algorithm computes the error in the resource consumption (g_t), which is the difference between uniform consumption over time and the actual resource consumption (below in the third gray box). A new dual variable for the next time period is computed using mirror descent based on the error, which then informs the next action. Mirror descent seeks to make the error as close as possible to zero, improving the accuracy of its estimate of the dual variable, so that resources are consumed uniformly over time. While the assumption of uniform resource consumption may be surprising, it helps avoid missing good opportunities and often aligns with commercial goals so is effective. Mirror descent also allows a variety of update rules; more details are in the paper.

An overview of the dual mirror descent algorithm.

By design, dual mirror descent has a self-correcting feature that prevents depleting resources too early or waiting too long to consume resources and missing good opportunities. When a request consumes more or less resources than the target, the corresponding dual variable is increased or decreased. When resources are then priced higher or lower, future actions are chosen to consume resources more conservatively or aggressively.

This algorithm is easy to implement, fast, and enjoys remarkable performance under different environments. These are some salient features of our algorithm:

• Existing methods require periodically solving large auxiliary optimization problems using past data. In contrast, this algorithm does not need to solve any auxiliary optimization problem and has a very simple rule to update the dual variables, which, in many cases, can be run in linear time complexity. Thus, it is appealing for many real-time applications that require fast decisions.
• There are minimal requirements on the structure of the problem. Such flexibility allows dual mirror descent to handle many applications across different sectors with minimal modifications. Moreover, our algorithms are flexible since they accommodate different objectives, constraints, or regularizers. By incorporating regularizers, decision makers can include important objectives beyond economic efficiency, such as fairness.
• Existing algorithms for online allocation problems are tailored for either adversarial or stochastic input data. Algorithms for adversarial inputs are robust as they make almost no assumptions on the structure of the data but, in turn, obtain performance guarantees that are too pessimistic in practice. On the other hand, algorithms for stochastic inputs enjoy better performance guarantees by exploiting statistical patterns in the data but can perform poorly when the model is misspecified. Dual mirror descent, however, attains performance close to optimal in both stochastic and adversarial input models while being oblivious to the structure of the input model. Compared to existing work on simultaneous approximation algorithms, our method is more general, applies to a wide range of problems, and requires no forecasts. Below is a comparison of our algorithm to other state-of-the-art methods. Results are based on synthetic data for an ad allocation problem.

Performance of dual mirror descent, a training based method, and an adversarial method relative to the optimal offline solution. Lower values indicate performance closer to the optimal offline allocation. Results are generated using synthetic experiments based on public data for an ad allocation problem.

Conclusion
In this post we introduced dual mirror descent, an algorithm for online allocation problems that is simple, robust, and flexible. It is particularly notable that after a long line of work in online allocation algorithms, dual mirror descent provides a way to analyze a wider range of algorithms with superior robustness priorities compared to previous techniques. Dual mirror descent has a wide range of applications across several commercial sectors and has been used over time at Google to help advertisers capture more value through better algorithmic decision making. We are also exploring further work related to mirror descent and its connections to PI controllers.

Acknowledgements
We would like to thank our co-authors Haihao Lu and Balu Sivan, and Kshipra Bhawalkar for their exceptional support and contributions. We would also like to thank our collaborators in the ad quality team and market algorithm research.

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¹Formalized in the equation below: ^↩

Posted by Xi Chen and Xiao Wang, Software Engineers, Google Research

Advanced language models (e.g., GPT, GLaM, PaLM and T5) have demonstrated diverse capabilities and achieved impressive results across tasks and languages by scaling up their number of parameters. Vision-language (VL) models can benefit from similar scaling to address many tasks, such as image captioning, visual question answering (VQA), object recognition, and in-context optical-character-recognition (OCR). Increasing the success rates for these practical tasks is important for everyday interactions and applications. Furthermore, for a truly universal system, vision-language models should be able to operate in many languages, not just one.

In “PaLI: A Jointly-Scaled Multilingual Language-Image Model”, we introduce a unified language-image model trained to perform many tasks and in over 100 languages. These tasks span vision, language, and multimodal image and language applications, such as visual question answering, image captioning, object detection, image classification, OCR, text reasoning, and others. Furthermore, we use a collection of public images that includes automatically collected annotations in 109 languages, which we call the WebLI dataset. The PaLI model pre-trained on WebLI achieves state-of-the-art performance on challenging image and language benchmarks, such as COCO-Captions, CC3M, nocaps, TextCaps, VQAv2, OK-VQA, TextVQA and others. It also outperforms prior models’ multilingual visual captioning and visual question answering benchmarks.

Overview
One goal of this project is to examine how language and vision models interact at scale and specifically the scalability of language-image models. We explore both per-modality scaling and the resulting cross-modal interactions of scaling. We train our largest model to 17 billion (17B) parameters, where the visual component is scaled up to 4B parameters and the language model to 13B. 

The PaLI model architecture is simple, reusable and scalable. It consists of a Transformer encoder that processes the input text, and an auto-regressive Transformer decoder that generates the output text. To process images, the input to the Transformer encoder also includes “visual words” that represent an image processed by a Vision Transformer (ViT). A key component of the PaLI model is reuse, in which we seed the model with weights from previously-trained uni-modal vision and language models, such as mT5-XXL and large ViTs. This reuse not only enables the transfer of capabilities from uni-modal training, but also saves computational cost.

The PaLI model addresses a wide range of tasks in the language-image, language-only and image-only domain using the same API (e.g., visual-question answering, image captioning, scene-text understanding, etc.). The model is trained to support over 100 languages and tuned to perform multilingually for multiple language-image tasks.

Dataset: Language-Image Understanding in 100+ Languages
Scaling studies for deep learning show that larger models require larger datasets to train effectively. To unlock the potential of language-image pretraining, we construct WebLI, a multilingual language-image dataset built from images and text available on the public web.

WebLI scales up the text language from English-only datasets to 109 languages, which enables us to perform downstream tasks in many languages. The data collection process is similar to that employed by other datasets, e.g. ALIGN and LiT, and enabled us to scale the WebLI dataset to 10 billion images and 12 billion alt-texts.

In addition to annotation with web text, we apply the Cloud Vision API to perform OCR on the images, leading to 29 billion image-OCR pairs. We perform near-deduplication of the images against the train, validation and test splits of 68 common vision and vision-language datasets, to avoid leaking data from downstream evaluation tasks, as is standard in the literature. To further improve the data quality, we score image and alt-text pairs based on their cross-modal similarity, and tune the threshold to keep only 10% of the images, for a total of 1 billion images used for training PaLI.

Sampled images from WebLI associated with multilingual alt-text and OCR. The second image is by jopradier (original), used under the CC BY-NC-SA 2.0 license. Remaining images are also used with permission.

Statistics of recognized languages from alt-text and OCR in WebLI.

Image-text pair counts of WebLI and other large-scale vision-language datasets, CLIP, ALIGN and LiT.

Training Large Language-Image Models
Vision-language tasks require different capabilities and sometimes have diverging goals. Some tasks inherently require localization of objects to solve the task accurately, whereas some other tasks might need a more global view. Similarly, different tasks might require either long or compact answers. To address all of these objectives, we leverage the richness of the WebLI pre-training data and introduce a mixture of pre-training tasks, which prepare the model for a variety of downstream applications. To accomplish the goal of solving a wide variety of tasks, we enable knowledge-sharing between multiple image and language tasks by casting all tasks into a single generalized API (input: image + text; output: text), which is also shared with the pretraining setup. The objectives used for pre-training are cast into the same API as a weighted mixture aimed at both maintaining the ability of the reused model components and training the model to perform new tasks (e.g., split-captioning for image description, OCR prediction for scene-text comprehension, VQG and VQA prediction).

The model is trained in JAX with Flax using the open-sourced T5X and Flaxformer framework. For the visual component, we introduce and train a large ViT architecture, named ViT-e, with 4B parameters using the open-sourced BigVision framework. ViT-e follows the same recipe as the ViT-G architecture (which has 2B parameters). For the language component, we concatenate the dense token embeddings with the patch embeddings produced by the visual component, together as the input to the multimodal encoder-decoder, which is initialized from mT5-XXL. During the training of PaLI, the weights of this visual component are frozen, and only the weights of the multimodal encoder-decoder are updated.

Results
We compare PaLI on common vision-language benchmarks that are varied and challenging. The PaLI model achieves state-of-the-art results on these tasks, even outperforming very large models in the literature. For example, it outperforms the Flamingo model, which is several times larger (80B parameters), on several VQA and image-captioning tasks, and it also sustains performance on challenging language-only and vision-only tasks, which were not the main training objective.

PaLI (17B parameters) outperforms the state-of-the-art approaches (including SimVLM, CoCa, GIT2, Flamingo, BEiT3) on multiple vision-and-language tasks. In this plot we show the absolute score differences compared with the previous best model to highlight the relative improvements of PaLI. Comparison is on the official test splits when available. CIDEr score is used for evaluation of the image captioning tasks, whereas VQA tasks are evaluated by VQA Accuracy.

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PaLI (17B parameters) outperforms the state-of-the-art approaches (including SimVLM, CoCa, GIT2, Flamingo, BEiT3) on multiple vision-and-language tasks. In this plot we show the absolute score differences compared with the previous best model to highlight the relative improvements of PaLI. Comparison is on the official test splits when available. CIDEr score is used for evaluation of the image captioning tasks, whereas VQA tasks are evaluated by VQA Accuracy.

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Model Scaling Results
We examine how the image and language model components interact with each other with regards to model scaling and where the model yields the most gains. We conclude that scaling both components jointly results in the best performance, and specifically, scaling the visual component, which requires relatively few parameters, is most essential. Scaling is also critical for better performance across multilingual tasks.

Scaling both the language and the visual components of the PaLI model contribute to improved performance. The plot shows the score differences compared to the PaLI-3B model: CIDEr score is used for evaluation of the image captioning tasks, whereas VQA tasks are evaluated by VQA Accuracy.

Multilingual captioning greatly benefits from scaling the PaLI models. We evaluate PaLI on a 35-language benchmark Crossmodal-3600. Here we present the average score over all 35 languages and the individual score for seven diverse languages.

Model Introspection: Model Fairness, Biases, and Other Potential Issues
To avoid creating or reinforcing unfair bias within large language and image models, important first steps are to (1) be transparent about the data that were used and how the model used those data, and (2) test for model fairness and conduct responsible data analyses. To address (1), our paper includes a data card and model card. To address (2), the paper includes results of demographic analyses of the dataset. We consider this a first step and know that it will be important to continue to measure and mitigate potential biases as we apply our model to new tasks, in alignment with our AI Principles.

Conclusion
We presented PaLI, a scalable multi-modal and multilingual model designed for solving a variety of vision-language tasks. We demonstrate improved performance across visual-, language- and vision-language tasks. Our work illustrates the importance of scale in both the visual and language parts of the model and the interplay between the two. We see that accomplishing vision and language tasks, especially in multiple languages, actually requires large scale models and data, and will potentially benefit from further scaling. We hope this work inspires further research in multi-modal and multilingual models.

Acknowledgements
We thank all the authors who conducted this research Soravit (Beer) Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari,Gaurav Mishra, Linting Xue, Ashish Thapliyal, James Bradbury, Weicheng Kuo, Mojtaba Seyedhosseini, Chao Jia, Burcu Karagol Ayan, Carlos Riquelme, Andreas Steiner, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut. We also thank Claire Cui, Slav Petrov, Tania Bedrax-Weiss, Joelle Barral, Tom Duerig, Paul Natsev, Fernando Pereira, Jeff Dean, Jeremiah Harmsen, Zoubin Ghahramani, Erica Moreira, Victor Gomes, Sarah Laszlo, Kathy Meier-Hellstern, Susanna Ricco, Rich Lee, Austin Tarango, Emily Denton, Bo Pang, Wei Li, Jihyung Kil, Tomer Levinboim, Julien Amelot, Zhenhai Zhu, Xiangning Chen, Liang Chen, Filip Pavetic, Daniel Keysers, Matthias Minderer, Josip Djolonga, Ibrahim Alabdulmohsin, Mostafa Dehghani, Yi Tay, Elizabeth Adkison, James Cockerille, Eric Ni, Anna Davies, and Maysam Moussalem for their suggestions, improvements and support. We thank Tom Small for providing visualizations for the blogpost.

Posted by Daniel Rebain, Student Researcher, and Mark Matthews, Senior Software Engineer, Google Research, Perception Team

An important aspect of human vision is our ability to comprehend 3D shape from the 2D images we observe. Achieving this kind of understanding with computer vision systems has been a fundamental challenge in the field. Many successful approaches rely on multi-view data, where two or more images of the same scene are available from different perspectives, which makes it much easier to infer the 3D shape of objects in the images.

There are, however, many situations where it would be useful to know 3D structure from a single image, but this problem is generally difficult or impossible to solve. For example, it isn’t necessarily possible to tell the difference between an image of an actual beach and an image of a flat poster of the same beach. However it is possible to estimate 3D structure based on what kind of 3D objects occur commonly and what similar structures look like from different perspectives.

In “LOLNeRF: Learn from One Look”, presented at CVPR 2022, we propose a framework that learns to model 3D structure and appearance from collections of single-view images. LOLNeRF learns the typical 3D structure of a class of objects, such as cars, human faces or cats, but only from single views of any one object, never the same object twice. We build our approach by combining Generative Latent Optimization (GLO) and neural radiance fields (NeRF) to achieve state-of-the-art results for novel view synthesis and competitive results for depth estimation.

We learn a 3D object model by reconstructing a large collection of single-view images using a neural network conditioned on latent vectors, z (left). This allows for a 3D model to be lifted from the image, and rendered from novel viewpoints. Holding the camera fixed, we can interpolate or sample novel identities (right).

Combining GLO and NeRF
GLO is a general method that learns to reconstruct a dataset (such as a set of 2D images) by co-learning a neural network (decoder) and table of codes (latents) that is also an input to the decoder. Each of these latent codes re-creates a single element (such as an image) from the dataset. Because the latent codes have fewer dimensions than the data elements themselves, the network is forced to generalize, learning common structure in the data (such as the general shape of dog snouts).

NeRF is a technique that is very good at reconstructing a static 3D object from 2D images. It represents an object with a neural network that outputs color and density for each point in 3D space. Color and density values are accumulated along rays, one ray for each pixel in a 2D image. These are then combined using standard computer graphics volume rendering to compute a final pixel color. Importantly, all these operations are differentiable, allowing for end-to-end supervision. By enforcing that each rendered pixel (of the 3D representation) matches the color of ground truth (2D) pixels, the neural network creates a 3D representation that can be rendered from any viewpoint.

We combine NeRF with GLO by assigning each object a latent code and concatenating it with standard NeRF inputs, giving it the ability to reconstruct multiple objects. Following GLO, we co-optimize these latent codes along with network weights during training to reconstruct the input images. Unlike standard NeRF, which requires multiple views of the same object, we supervise our method with only single views of any one object (but multiple examples of that type of object). Because NeRF is inherently 3D, we can then render the object from arbitrary viewpoints. Combining NeRF with GLO gives it the ability to learn common 3D structure across instances from only single views while still retaining the ability to recreate specific instances of the dataset.

Camera Estimation
In order for NeRF to work, it needs to know the exact camera location, relative to the object, for each image. Unless this was measured when the image was taken, it is generally unknown. Instead, we use the MediaPipe Face Mesh to extract five landmark locations from the images. Each of these 2D predictions correspond to a semantically consistent point on the object (e.g., the tip of the nose or corners of the eyes). We can then derive a set of canonical 3D locations for the semantic points, along with estimates of the camera poses for each image, such that the projection of the canonical points into the images is as consistent as possible with the 2D landmarks.

We train a per-image table of latent codes alongside a NeRF model. Output is subject to per-ray RGB, mask and hardness losses. Cameras are derived from a fit of predicted landmarks to canonical 3D keypoints.

Example MediaPipe landmarks and segmentation masks (images from CelebA).

Hard Surface and Mask Losses
Standard NeRF is effective for accurately reproducing the images, but in our single-view case, it tends to produce images that look blurry when viewed off-axis. To address this, we introduce a novel hard surface loss, which encourages the density to adopt sharp transitions from exterior to interior regions, reducing blurring. This essentially tells the network to create “solid” surfaces, and not semi-transparent ones like clouds.

We also obtained better results by splitting the network into separate foreground and background networks. We supervised this separation with a mask from the MediaPipe Selfie Segmenter and a loss to encourage network specialization. This allows the foreground network to specialize only on the object of interest, and not get “distracted” by the background, increasing its quality.

Results
We surprisingly found that fitting only five key points gave accurate enough camera estimates to train a model for cats, dogs, or human faces. This means that given only a single view of your beloved cats Schnitzel, Widget and friends, you can create a new image from any other angle.

Top: example cat images from AFHQ. Bottom: A synthesis of novel 3D views created by LOLNeRF.

Conclusion
We’ve developed a technique that is effective at discovering 3D structure from single 2D images. We see great potential in LOLNeRF for a variety of applications and are currently investigating potential use-cases.

Interpolation of feline identities from linear interpolation of learned latent codes for different examples in AFHQ.

Code Release
We acknowledge the potential for misuse and importance of acting responsibly. To that end, we will only release the code for reproducibility purposes, but will not release any trained generative models.

Acknowledgements
We would like to thank Andrea Tagliasacchi, Kwang Moo Yi, Viral Carpenter, David Fleet, Danica Matthews, Florian Schroff, Hartwig Adam and Dmitry Lagun for continuous help in building this technology.

Posted by Yuxiang Yang, Student Researcher, Robotics at Google

An important promise for quadrupedal robots is their potential to operate in complex outdoor environments that are difficult or inaccessible for humans. Whether it’s to find natural resources deep in the mountains, or to search for life signals in heavily-damaged earthquake sites, a robust and versatile quadrupedal robot could be very helpful. To achieve that, a robot needs to perceive the environment, understand its locomotion challenges, and adapt its locomotion skill accordingly. While recent advances in perceptive locomotion have greatly enhanced the capability of quadrupedal robots, most works focus on indoor or urban environments, thus they cannot effectively handle the complexity of off-road terrains. In these environments, the robot needs to understand not only the terrain shape (e.g., slope angle, smoothness), but also its contact properties (e.g., friction, restitution, deformability), which are important for a robot to decide its locomotion skills. As existing perceptive locomotion systems mostly focus on the use of depth cameras or LiDARs, it can be difficult for these systems to estimate such terrain properties accurately.

In “Learning Semantics-Aware Locomotion Skills from Human Demonstrations”, we design a hierarchical learning framework to improve a robot’s ability to traverse complex, off-road environments. Unlike previous approaches that focus on environment geometry, such as terrain shape and obstacle locations, we focus on environment semantics, such as terrain type (grass, mud, etc.) and contact properties, which provide a complementary set of information useful for off-road environments. As the robot walks, the framework decides the locomotion skill, including the speed and gait (i.e., shape and timing of the legs’ movement) of the robot based on the perceived semantics, which allows the robot to walk robustly on a variety of off-road terrains, including rocks, pebbles, deep grass, mud, and more.

Our framework selects skills (gait and speed) of the robot from the camera RGB image. We first compute the speed from terrain semantics, and then select a gait based on the speed.

Overview
The hierarchical framework consists of a high-level skill policy and a low level motor controller. The skill policy selects a locomotion skill based on camera images, and the motor controller converts the selected skill into motor commands. The high-level skill policy is further decomposed into a learned speed policy and a heuristic-based gait selector. To decide a skill, the speed policy first computes the desired forward speed, based on the semantic information from the onboard RGB camera. For energy efficiency and robustness, quadrupedal robots usually select a different gait for each speed, so we designed the gait selector to compute a desired gait based on the forward speed. Lastly, a low-level convex model-predictive controller (MPC) converts the desired locomotion skill into motor torque commands, and executes them on the real hardware. We train the speed policy directly in the real world using imitation learning because it requires fewer training data compared to standard reinforcement learning algorithms.

The framework consists of a high-level skill policy and a low-level motor controller.

Learning Speed Command from Human Demonstrations
As the central component in our pipeline, the speed policy outputs the desired forward speed of the robot based on the RGB image from the onboard camera. Although many robot learning tasks can leverage simulation as a source of lower-cost data collection, we train the speed policy in the real world because accurate simulation of complex and diverse off-road environments is not yet available. As policy learning in the real world is time-consuming and potentially unsafe, we make two key design choices to improve the data efficiency and safety of our system.

The first is learning from human demonstrations. Standard reinforcement learning algorithms typically learn by exploration, where the agent attempts different actions in an environment and builds preferences based on the rewards received. However, such explorations can be potentially unsafe, especially in off-road environments, since any robot failures can damage both the robot hardware and the surrounding environment. To ensure safety, we train the speed policy using imitation learning from human demonstrations. We first ask a human operator to teleoperate the robot on a variety of off-road terrains, where the operator controls the speed and heading of the robot using a remote joystick. Next, we collect the training data by storing (image, forward_speed) pairs. We then train the speed policy using standard supervised learning to predict the human operator’s speed command. As it turns out, the human demonstration is both safe and high-quality, and allows the robot to learn a proper speed choice for different terrains.

The second key design choice is the training method. Deep neural networks, especially those involving high-dimensional visual inputs, typically require lots of data to train. To reduce the amount of real-world training data required, we first pre-train a semantic segmentation model on RUGD (an off-road driving dataset where the images look similar to those captured by the robot’s onboard camera), where the model predicts the semantic class (grass, mud, etc.) for every pixel in the camera image. We then extract a semantic embedding from the model’s intermediate layers and use that as the feature for on-robot training. With the pre-trained semantic embedding, we can train the speed policy effectively using less than 30 minutes of real-world data, which greatly reduces the amount of effort required.

We pre-train a semantic segmentation model and extract a semantic embedding to be fine-tuned on robot data.

Gait Selection and Motor Control
The next component in the pipeline, the gait selector, computes the appropriate gait based on the speed command from the speed policy. The gait of a robot, including its stepping frequency, swing height, and base height, can greatly affect the robot’s ability to traverse different terrains.

Scientific studies have shown that animals switch between different gaits at different speeds, and this result is further validated in quadrupedal robots, so we designed the gait selector to compute a robust gait for each speed. Compared to using a fixed gait across all speeds, we find that the gait selector further enhances the robot’s navigation performance on off-road terrains (more details in the paper).

The last component of the pipeline is a motor controller, which converts the speed and gait commands into motor torques. Similar to previous work, we use separate control strategies for swing and stance legs. By separating the task of skill learning and motor control, the skill policy only needs to output the desired speed, and does not need to learn low-level locomotion controls, which greatly simplifies the learning process.

Experiment Results
We implemented our framework on an A1 quadrupedal robot and tested it on an outdoor trail with multiple terrain types, including grass, gravel, and asphalt, which pose varying degrees of difficulty for the robot. For example, while the robot needs to walk slowly with high foot swings in deep grass to prevent its foot from getting stuck, on asphalt it can walk much faster with lower foot swings for better energy efficiency. Our framework captures such differences and selects an appropriate skill for each terrain type: slow speed (0.5m/s) on deep grass, medium speed (1m/s) on gravel, and high speed (1.4m/s) on asphalt. It completes the 460m-long trail in 9.6 minutes with an average speed of 0.8m/s (i.e., that’s 1.8 miles or 2.9 kilometers per hour). In contrast, non-adaptive policies either cannot complete the trail safely or walk significantly slower (0.5m/s), illustrating the importance of adapting locomotion skills based on the perceived environments.

The framework selects different speeds based on conditions of the trail.

To test generalizability, we also deployed the robot to a number of trails that are not seen during training. The robot traverses through all of them without failure, and adjusts its locomotion skills based on terrain semantics. In general, the skill policy selects a faster skill on rigid and flat terrains and a slower speed on deformable or uneven terrain. At the time of writing, the robot has traversed over 6km of outdoor trails without failure.

With the framework, the robot walks safely on a variety of outdoor terrains not seen during training.

Conclusion
In this work, we present a hierarchical framework to learn semantic-aware locomotion skills for off-road locomotion. Using less than 30 minutes of human demonstration data, the framework learns to adjust the speed and gait of the robot based on the perceived semantics of the environment. The robot can walk safely and efficiently on a wide variety of off-road terrains. One limitation of our framework is that it only adjusts locomotion skills for standard walking and does not support more agile behaviors such as jumping, which can be essential for traversing more difficult terrains with gaps or hurdles. Another limitation is that our framework currently requires manual steering commands to follow a desired path and reach the goal. In future work, we plan to look into a deeper integration of high-level skill policy with the low-level controller for more agile behaviors, and incorporate navigation and path planning into the framework so that the robot can operate fully autonomously in challenging off-road environments.

Acknowledgements
We would like to thank our paper co-authors: Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan, and Byron Boots. We would also like to thank the team members of Robotics at Google for discussions and feedback.

Posted by Zhengzhong Tu and Yinxiao Li, Software Engineers, Google Research

Convolutional neural networks have been the dominant machine learning architecture for computer vision since the introduction of AlexNet in 2012. Recently, inspired by the evolution of Transformers in natural language processing, attention mechanisms have been prominently incorporated into vision models. These attention methods boost some parts of the input data while minimizing other parts so that the network can focus on small but important parts of the data. The Vision Transformer (ViT) has created a new landscape of model designs for computer vision that is completely free of convolution. ViT regards image patches as a sequence of words, and applies a Transformer encoder on top. When trained on sufficiently large datasets, ViT demonstrates compelling performance on image recognition.

While convolutions and attention are both sufficient for good performance, neither of them are necessary. For example, MLP-Mixer adopts a simple multi-layer perceptron (MLP) to mix image patches across all the spatial locations, resulting in an all-MLP architecture. It is a competitive alternative to existing state-of-the-art vision models in terms of the trade-off between accuracy and computation required for training and inference. However, both ViT and the MLP models struggle to scale to higher input resolution because the computational complexity increases quadratically with respect to the image size.

Today we present a new multi-axis approach that is simple and effective, improves on the original ViT and MLP models, can better adapt to high-resolution, dense prediction tasks, and can naturally adapt to different input sizes with high flexibility and low complexity. Based on this approach, we have built two backbone models for high-level and low-level vision tasks. We describe the first in “MaxViT: Multi-Axis Vision Transformer”, to be presented in ECCV 2022, and show it significantly improves the state of the art for high-level tasks, such as image classification, object detection, segmentation, quality assessment, and generation. The second, presented in “MAXIM: Multi-Axis MLP for Image Processing” at CVPR 2022, is based on a UNet-like architecture and achieves competitive performance on low-level imaging tasks including denoising, deblurring, dehazing, deraining, and low-light enhancement. To facilitate further research on efficient Transformer and MLP models, we have open-sourced the code and models for both MaxViT and MAXIM.

A demo of image deblurring using MAXIM frame by frame.

Overview
Our new approach is based on multi-axis attention, which decomposes the full-size attention (each pixel attends to all the pixels) used in ViT into two sparse forms — local and (sparse) global. As shown in the figure below, the multi-axis attention contains a sequential stack of block attention and grid attention. The block attention works within non-overlapping windows (small patches in intermediate feature maps) to capture local patterns, while the grid attention works on a sparsely sampled uniform grid for long-range (global) interactions. The window sizes of grid and block attentions can be fully controlled as hyperparameters to ensure a linear computational complexity to the input size.

The proposed multi-axis attention conducts blocked local and dilated global attention sequentially followed by a FFN, with only a linear complexity. The pixels in the same colors are attended together.

Such low-complexity attention can significantly improve its wide applicability to many vision tasks, especially for high-resolution visual predictions, demonstrating greater generality than the original attention used in ViT. We build two backbone instantiations out of this multi-axis attention approach – MaxViT and MAXIM, for high-level and low-level tasks, respectively.

MaxViT
In MaxViT, we first build a single MaxViT block (shown below) by concatenating MBConv (proposed by EfficientNet, V2) with the multi-axis attention. This single block can encode local and global visual information regardless of input resolution. We then simply stack repeated blocks composed of attention and convolutions in a hierarchical architecture (similar to ResNet, CoAtNet), yielding our homogenous MaxViT architecture. Notably, MaxViT is distinguished from previous hierarchical approaches as it can “see” globally throughout the entire network, even in earlier, high-resolution stages, demonstrating stronger model capacity on various tasks.

The meta-architecture of MaxViT.

MAXIM
Our second backbone, MAXIM, is a generic UNet-like architecture tailored for low-level image-to-image prediction tasks. MAXIM explores parallel designs of the local and global approaches using the gated multi-layer perceptron (gMLP) network (patching-mixing MLP with a gating mechanism). Another contribution of MAXIM is the cross-gating block that can be used to apply interactions between two different input signals. This block can serve as an efficient alternative to the cross-attention module as it only employs the cheap gated MLP operators to interact with various inputs without relying on the computationally heavy cross-attention. Moreover, all the proposed components including the gated MLP and cross-gating blocks in MAXIM enjoy linear complexity to image size, making it even more efficient when processing high-resolution pictures.

Results
We demonstrate the effectiveness of MaxViT on a broad range of vision tasks. On image classification, MaxViT achieves state-of-the-art results under various settings: with only ImageNet-1K training, MaxViT attains 86.5% top-1 accuracy; with ImageNet-21K (14M images, 21k classes) pre-training, MaxViT achieves 88.7% top-1 accuracy; and with JFT (300M images, 18k classes) pre-training, our largest model MaxViT-XL achieves a high accuracy of 89.5% with 475M parameters.

Performance comparison of MaxViT with state-of-the-art models on ImageNet-1K. Top: Accuracy vs. FLOPs performance scaling with 224×224 image resolution. Bottom: Accuracy vs. parameters scaling curve under ImageNet-1K fine-tuning setting.

For downstream tasks, MaxViT as a backbone delivers favorable performance on a broad spectrum of tasks. For object detection and segmentation on the COCO dataset, the MaxViT backbone achieves 53.4 AP, outperforming other base-level models while requiring only about 60% the computational cost. For image aesthetics assessment, the MaxViT model advances the state-of-the-art MUSIQ model by 3.5% in terms of linear correlation with human opinion scores. The standalone MaxViT building block also demonstrates effective performance on image generation, achieving better FID and IS scores on the ImageNet-1K unconditional generation task with a significantly lower number of parameters than the state-of-the-art model, HiT.

The UNet-like MAXIM backbone, customized for image processing tasks, has also demonstrated state-of-the-art results on 15 out of 20 tested datasets, including denoising, deblurring, deraining, dehazing, and low-light enhancement, while requiring fewer or comparable number of parameters and FLOPs than competitive models. Images restored by MAXIM show more recovered details with less visual artifacts.

Visual results of MAXIM for image deblurring, deraining, and low-light enhancement.

Summary
Recent works in the last two or so years have shown that ConvNets and Vision Transformers can achieve similar performance. Our work presents a unified design that takes advantage of the best of both worlds — efficient convolution and sparse attention — and demonstrates that a model built on top, namely MaxViT, can achieve state-of-the-art performance on a variety of vision tasks. More importantly, MaxViT scales well to very large data sizes. We also show that an alternative multi-axis design using MLP operators, MAXIM, achieves state-of-the-art performance on a broad range of low-level vision tasks.

Even though we present our models in the context of vision tasks, the proposed multi-axis approach can easily extend to language modeling to capture both local and global dependencies in linear time. Motivated by the work here, we expect that it is worthwhile to study other forms of sparse attention in higher-dimensional or multimodal signals such as videos, point clouds, and vision-language models.

We have open-sourced the code and models of MAXIM and MaxViT to facilitate future research on efficient attention and MLP models.

Acknowledgments
We would like to thank our co-authors: Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, and Alan Bovik. We would also like to acknowledge the valuable discussion and support from Xianzhi Du, Long Zhao, Wuyang Chen, Hanxiao Liu, Zihang Dai, Anurag Arnab, Sungjoon Choi, Junjie Ke, Mauricio Delbracio, Irene Zhu, Innfarn Yoo, Huiwen Chang, and Ce Liu.

Posted by Richard C. Gerkin, Google Research, and Alexander B. Wiltschko, Google

Did you ever try to measure a smell? …Until you can measure their likenesses and differences you can have no science of odor. If you are ambitious to found a new science, measure a smell.

— Alexander Graham Bell, 1914.

How can we measure a smell? Smells are produced by molecules that waft through the air, enter our noses, and bind to sensory receptors. Potentially billions of molecules can produce a smell, so figuring out which ones produce which smells is difficult to catalog or predict. Sensory maps can help us solve this problem. Color vision has the most familiar examples of these maps, from the color wheel we each learn in primary school to more sophisticated variants used to perform color correction in video production. While these maps have existed for centuries, useful maps for smell have been missing, because smell is a harder problem to crack: molecules vary in many more ways than photons do; data collection requires physical proximity between the smeller and smell (we don’t have good smell “cameras” and smell “monitors”); and the human eye only has three sensory receptors for color while the human nose has > 300 for odor. As a result, previous efforts to produce odor maps have failed to gain traction.

In 2019, we developed a graph neural network (GNN) model that began to explore thousands of examples of distinct molecules paired with the smell labels that they evoke, e.g., “beefy”, “floral”, or “minty”, to learn the relationship between a molecule’s structure and the probability that such a molecule would have each smell label. The embedding space of this model contains a representation of each molecule as a fixed-length vector describing that molecule in terms of its odor, much as the RGB value of a visual stimulus describes its color.

Left: An example of a color map (CIE 1931) in which coordinates can be directly translated into values for hue and saturation. Similar colors lie near each other, and specific wavelengths of light (and combinations thereof) can be identified with positions on the map. Right: Odors in the Principal Odor Map operate similarly. Individual molecules correspond to points (grey), and the locations of these points reflect predictions of their odor character.

Today we introduce the “Principal Odor Map” (POM), which identifies the vector representation of each odorous molecule in the model’s embedding space as a single point in a high-dimensional space. The POM has the properties of a sensory map: first, pairs of perceptually similar odors correspond to two nearby points in the POM (by analogy, red is nearer to orange than to green on the color wheel). Second, the POM enables us to predict and discover new odors and the molecules that produce them. In a series of papers, we demonstrate that the map can be used to prospectively predict the odor properties of molecules, understand these properties in terms of fundamental biology, and tackle pressing global health problems. We discuss each of these promising applications of the POM and how we test them below.

Test 1: Challenging the Model with Molecules Never Smelled Before
First, we asked if the underlying model could correctly predict the odors of new molecules that no one had ever smelled before and that were very different from molecules used during model development. This is an important test — many models perform well on data that looks similar to what the model has seen before, but break down when tested on novel cases.

To test this, we collected the largest ever dataset of odor descriptions for novel molecules. Our partners at the Monell Center trained panelists to rate the smell of each of 400 molecules using 55 distinct labels (e.g., “minty”) that were selected to cover the space of possible smells while being neither redundant nor too sparse. Unsurprisingly, we found that different people had different characterizations of the same molecule. This is why sensory research typically uses panels of dozens or hundreds of people and highlights why smell is a hard problem to solve. Rather than see if the model could match any one person, we asked how close it was to the consensus: the average across all of the panelists. We found that the predictions of the model were closer to the consensus than the average panelist was. In other words, the model demonstrated an exceptional ability to predict odor from a molecule’s structure.

Predictions made by two models, our GNN model (orange) and a baseline chemoinformatic random forest (RF) model (blue), compared with the mean ratings given by trained panelists (green) for the molecule 2,3-dihydrobenzofuran-5-carboxaldehyde. Each bar corresponds to one odor character label (with only the top 17 of 55 shown for clarity). The top five are indicated in color; our model correctly identifies four of the top five, with high confidence, vs. only three of five, with low confidence, for the RF model. The correlation (R) to the full set of 55 labels is also higher in our model.

Unlike alternative benchmark models (RF and nearest-neighbor models trained on various sets of chemoinformatic features), our GNN model outperforms the median human panelist at predicting the panel mean rating. In other words, our GNN model better reflects the panel consensus than the typical panelist.

The POM also exhibited state-of-the-art performance on alternative human olfaction tasks like detecting the strength of a smell or the similarity of different smells. Thus, with the POM, it should be possible to predict the odor qualities of any of billions of as-yet-unknown odorous molecules, with broad applications to flavor and fragrance.

Test 2: Linking Odor Quality Back to Fundamental Biology
Because the Principal Odor Map was useful in predicting human odor perception, we asked whether it could also predict odor perception in animals, and the brain activity that underlies it. We found that the map could successfully predict the activity of sensory receptors, neurons, and behavior in most animals that olfactory neuroscientists have studied, including mice and insects.

What common feature of the natural world makes this map applicable to species separated by hundreds of millions of years of evolution? We realized that the common purpose of the ability to smell might be to detect and discriminate between metabolic states, i.e., to sense when something is ripe vs. rotten, nutritious vs. inert, or healthy vs. sick. We gathered data about metabolic reactions in dozens of species across the kingdoms of life and found that the map corresponds closely to metabolism itself. When two molecules are far apart in odor, according to the map, a long series of metabolic reactions is required to convert one to the other; by contrast, similarly smelling molecules are separated by just one or a few reactions. Even long reaction pathways containing many steps trace smooth paths through the map. And molecules that co-occur in the same natural substances (e.g., an orange) are often very tightly clustered on the map. The POM shows that olfaction is linked to our natural world through the structure of metabolism and, perhaps surprisingly, captures fundamental principles of biology.

Left: We aggregated metabolic reactions found in 17 species across 4 kingdoms to construct a metabolic graph. In this illustration, each circle is a distinct metabolite molecule and an arrow indicates that there is a metabolic reaction that converts one molecule to another. Some metabolites have an odor (color) and others do not (gray), and the metabolic distance between two odorous metabolites is the minimum number of reactions necessary to convert one into the other. In the path shown in bold, the distance is 3. Right: Metabolic distance was highly correlated with distance in the POM, an estimate of perceived odor dissimilarity.

Test 3: Extending the Model to Tackle a Global Health Challenge
A map of odor that is tightly connected to perception and biology across the animal kingdom opens new doors. Mosquitos and other insect pests are drawn to humans in part by their odor perception. Since the POM can be used to predict animal olfaction generally, we retrained it to tackle one of humanity’s biggest problems, the scourge of diseases transmitted by mosquitoes and ticks, which kill hundreds of thousands of people each year.

For this purpose, we improved our original model with two new sources of data: (1) a long-forgotten set of experiments conducted by the USDA on human volunteers beginning 80 years ago and recently made discoverable by Google Books, which we subsequently made machine-readable; and (2) a new dataset collected by our partners at TropIQ, using their high-throughput laboratory mosquito assay. Both datasets measure how well a given molecule keeps mosquitos away. Together, the resulting model can predict the mosquito repellency of nearly any molecule, enabling a virtual screen over huge swaths of molecular space. We validated this screen experimentally using entirely new molecules and found over a dozen of them with repellency at least as high as DEET, the active ingredient in most insect repellents. Less expensive, longer lasting, and safer repellents can reduce the worldwide incidence of diseases like malaria, potentially saving countless lives.

We digitized USDA mosquito repellency data for thousands of molecules previously scanned by Google Books, and used it to refine the learned representation (the map) at the heart of the model. We added additional layers, specifically to predict repellency in a mosquito feeder assay, and iteratively trained the model to improve assay predictions while running computational screens for candidate repellents.

Many molecules showing mosquito repellency in the laboratory assay also showed repellency when applied to humans. Several showed repellency greater than the most common repellents used today (DEET and picaridin).

The Road Ahead
We discovered that our modeling approach to smell prediction could be used to draw a Principal Odor Map for tackling odor-related problems more generally. This map was the key to measuring smell: it answered a range of questions about novel smells and the molecules that produce them, it connected smells back to their origins in evolution and the natural world, and it is helping us tackle important human-health challenges that affect millions of people. Going forward, we hope that this approach can be used to find new solutions to problems in food and fragrance formulation, environmental quality monitoring, and the detection of human and animal diseases.

Acknowledgements
This work was performed by the ML olfaction research team, including Benjamin Sanchez-Lengeling, Brian K. Lee, Jennifer N. Wei, Wesley W. Qian, and Jake Yasonik (the latter two were partly supported by the Google Student Researcher program) and our external partners including Emily Mayhew and Joel D. Mainland from the Monell Center, and Koen Dechering and Marnix Vlot from TropIQ. The Google Books team brought the USDA dataset online. Richard C. Gerkin was supported by the Google Visiting Faculty Researcher program and is also an Associate Research Professor at Arizona State University.

Posted Grigor Aslanyan, Software Engineer, Google

Patent documents typically use legal and highly technical language, with context-dependent terms that may have meanings quite different from colloquial usage and even between different documents. The process of using traditional patent search methods (e.g., keyword searching) to search through the corpus of over one hundred million patent documents can be tedious and result in many missed results due to the broad and non-standard language used. For example, a “soccer ball” may be described as a “spherical recreation device”, “inflatable sportsball” or “ball for ball game”. Additionally, the language used in some patent documents may obfuscate terms to their advantage, so more powerful natural language processing (NLP) and semantic similarity understanding can give everyone access to do a thorough search.

The patent domain (and more general technical literature like scientific publications) poses unique challenges for NLP modeling due to its use of legal and technical terms. While there are multiple commonly used general-purpose semantic textual similarity (STS) benchmark datasets (e.g., STS-B, SICK, MRPC, PIT), to the best of our knowledge, there are currently no datasets focused on technical concepts found in patents and scientific publications (the somewhat related BioASQ challenge contains a biomedical question answering task). Moreover, with the continuing growth in size of the patent corpus (millions of new patents are issued worldwide every year), there is a need to develop more useful NLP models for this domain.

Today, we announce the release of the Patent Phrase Similarity dataset, a new human-rated contextual phrase-to-phrase semantic matching dataset, and the accompanying paper, presented at the SIGIR PatentSemTech Workshop, which focuses on technical terms from patents. The Patent Phrase Similarity dataset contains ~50,000 rated phrase pairs, each with a Cooperative Patent Classification (CPC) class as context. In addition to similarity scores that are typically included in other benchmark datasets, we include granular rating classes similar to WordNet, such as synonym, antonym, hypernym, hyponym, holonym, meronym, and domain related. This dataset (distributed under the Creative Commons Attribution 4.0 International license) was used by Kaggle and USPTO as the benchmark dataset in the U.S. Patent Phrase to Phrase Matching competition to draw more attention to the performance of machine learning models on technical text. Initial results show that models fine-tuned on this new dataset perform substantially better than general pre-trained models without fine-tuning.

The Patent Phrase Similarity Dataset
To better train the next generation of state-of-the-art models, we created the Patent Phrase Similarity dataset, which includes many examples to address the following problems: (1) phrase disambiguation, (2) adversarial keyword matching, and (3) hard negative keywords (i.e., keywords that are unrelated but received a high score for similarity from other models ). Some keywords and phrases can have multiple meanings (e.g., the phrase “mouse” may refer to an animal or a computer input device), so we disambiguate the phrases by including CPC classes with each pair of phrases. Also, many NLP models (e.g., bag of words models) will not do well on data with phrases that have matching keywords but are otherwise unrelated (adversarial keywords, e.g., “container section” → “kitchen container”, “offset table” → “table fan”). The Patent Phrase Similarity dataset is designed to include many examples of matching keywords that are unrelated through adversarial keyword match, enabling NLP models to improve their performance.

Each entry in the Patent Phrase Similarity dataset contains two phrases, an anchor and target, a context CPC class, a rating class, and a similarity score. The dataset contains 48,548 entries with 973 unique anchors, split into training (75%), validation (5%), and test (20%) sets. When splitting the data, all of the entries with the same anchor are kept together in the same set. There are 106 different context CPC classes and all of them are represented in the training set.

Anchor	Target	Context	Rating	Score
acid absorption	absorption of acid	B08	exact	1.0
acid absorption	acid immersion	B08	synonym	0.75
acid absorption	chemically soaked	B08	domain related	0.25
acid absorption	acid reflux	B08	not related	0.0
gasoline blend	petrol blend	C10	synonym	0.75
gasoline blend	fuel blend	C10	hypernym	0.5
gasoline blend	fruit blend	C10	not related	0.0
faucet assembly	water tap	A22	hyponym	0.5
faucet assembly	water supply	A22	holonym	0.25
faucet assembly	school assembly	A22	not related	0.0

A small sample of the dataset with anchor and target phrases, context CPC class (B08: Cleaning, C10: Petroleum, gas, fuel, lubricants, A22: Butchering, processing meat/poultry/fish), a rating class, and a similarity score.

Generating the Dataset
To generate the Patent Phrase Similarity data, we first process the ~140 million patent documents in the Google Patent’s corpus and automatically extract important English phrases, which are typically noun phrases (e.g., “fastener”, “lifting assembly”) and functional phrases (e.g., “food processing”, “ink printing”). Next, we filter and keep phrases that appear in at least 100 patents and randomly sample around 1,000 of these filtered phrases, which we call anchor phrases. For each anchor phrase, we find all of the matching patents and all of the CPC classes for those patents. We then randomly sample up to four matching CPC classes, which become the context CPC classes for the specific anchor phrase.

We use two different methods for pre-generating target phrases: (1) partial matching and (2) a masked language model (MLM). For partial matching, we randomly select phrases from the entire corpus that partially match with the anchor phrase (e.g., “abatement” → “noise abatement”, “material formation” → “formation material”). For MLM, we select sentences from the patents that contain a given anchor phrase, mask them out, and use the Patent-BERT model to predict candidates for the masked portion of the text. Then, all of the phrases are cleaned up, which includes lowercasing and the removal of punctuation and certain stopwords (e.g., “and”, “or”, “said”), and sent to expert raters for review. Each phrase pair is rated independently by two raters skilled in the technology area. Each rater also generates new target phrases with different ratings. Specifically, they are asked to generate some low-similarity and unrelated targets that partially match with the original anchor and/or some high-similarity targets. Finally, the raters meet to discuss their ratings and come up with final ratings.

Dataset Evaluation
To evaluate its performance, the Patent Phrase Similarity dataset was used in the U.S. Patent Phrase to Phrase Matching Kaggle competition. The competition was very popular, drawing about 2,000 competitors from around the world. A variety of approaches were successfully used by the top scoring teams, including ensemble models of BERT variants and prompting (see the full discussion for more details). The table below shows the best results from the competition, as well as several off-the-shelf baselines from our paper. The Pearson correlation metric was used to measure the linear correlation between the predicted and true scores, which is a helpful metric to target for downstream models so they can distinguish between different similarity ratings.

The baselines in the paper can be considered zero-shot in the sense that they use off-the-shelf models without any further fine-tuning on the new dataset (we use these models to embed the anchor and target phrases separately and compute the cosine similarity between them). The Kaggle competition results demonstrate that by using our training data, one can achieve significant improvements compared with existing NLP models. We have also estimated human performance on this task by comparing a single rater’s scores to the combined score of both raters. The results indicate that this is not a particularly easy task, even for human experts.

Model	Training	Pearson correlation
word2vec	Zero-shot	0.44
Patent-BERT	Zero-shot	0.53
Sentence-BERT	Zero-shot	0.60
Kaggle 1st place single	Fine-tuned	0.87
Kaggle 1st place ensemble	Fine-tuned	0.88
Human		0.93

Performance of popular models with no fine-tuning (zero-shot), models fine-tuned on the Patent Phrase Similarity dataset as part of the Kaggle competition, and single human performance.

Conclusion and Future Work
We present the Patent Phrase Similarity dataset, which was used as the benchmark dataset in the U.S. Patent Phrase to Phrase Matching competition, and demonstrate that by using our training data, one can achieve significant improvements compared with existing NLP models.

Additional challenging machine learning benchmarks can be generated from the patent corpus, and patent data has made its way into many of today’s most-studied models. For example, the C4 text dataset used to train T5 contains many patent documents. The BigBird and LongT5 models also use patents via the BIGPATENT dataset. The availability, breadth and open usage terms of full text data (see Google Patents Public Datasets) makes patents a unique resource for the research community. Possibilities for future tasks include massively multi-label classification, summarization, information retrieval, image-text similarity, citation graph prediction, and translation. See the paper for more details.

Acknowledgements
This work was possible through a collaboration with Kaggle, Satsyil Corp., USPTO, and MaxVal. Thanks to contributors Ian Wetherbee from Google, Will Cukierski and Maggie Demkin from Kaggle. Thanks to Jerry Ma, Scott Beliveau, and Jamie Holcombe from USPTO and Suja Chittamahalingam from MaxVal for their contributions.

Posted by Tingbo Hou and Juhyun Lee, Software Engineers, Google

In recent years video conferencing has played an increasingly important role in both work and personal communication for many users. Over the past two years, we have enhanced this experience in Google Meet by introducing privacy-preserving machine learning (ML) powered background features, also known as “virtual green screen”, which allows users to blur their backgrounds or replace them with other images. What is unique about this solution is that it runs directly in the browser without the need to install additional software.

So far, these ML-powered features have relied on CPU inference made possible by leveraging neural network sparsity, a common solution that works across devices, from entry level computers to high-end workstations. This enables our features to reach the widest audience. However, mid-tier and high-end devices often have powerful GPUs that remain untapped for ML inference, and existing functionality allows web browsers to access GPUs via shaders (WebGL).

With the latest update to Google Meet, we are now harnessing the power of GPUs to significantly improve the fidelity and performance of these background effects. As we detail in “Efficient Heterogeneous Video Segmentation at the Edge”, these advances are powered by two major components: 1) a novel real-time video segmentation model and 2) a new, highly efficient approach for in-browser ML acceleration using WebGL. We leverage this capability to develop fast ML inference via fragment shaders. This combination results in substantial gains in accuracy and latency, leading to crisper foreground boundaries.

CPU segmentation vs. HD segmentation in Meet.

Moving Towards Higher Quality Video Segmentation Models
To predict finer details, our new segmentation model now operates on high definition (HD) input images, rather than lower-resolution images, effectively doubling the resolution over the previous model. To accommodate this, the model must be of higher capacity to extract features with sufficient detail. Roughly speaking, doubling the input resolution quadruples the computation cost during inference.

Inference of high-resolution models using the CPU is not feasible for many devices. The CPU may have a few high-performance cores that enable it to execute arbitrary complex code efficiently, but it is limited in its ability for the parallel computation required for HD segmentation. In contrast, GPUs have many, relatively low-performance cores coupled with a wide memory interface, making them uniquely suitable for high-resolution convolutional models. Therefore, for mid-tier and high-end devices, we adopt a significantly faster pure GPU pipeline, which is integrated using WebGL.

This change inspired us to revisit some of the prior design decisions for the model architecture.

• Backbone: We compared several widely-used backbones for on-device networks and found EfficientNet-Lite to be a better fit for the GPU because it removes the squeeze-and-excitation block, a component that is inefficient on WebGL (more below).
• Decoder: We switched to a multi-layer perceptron (MLP) decoder consisting of 1×1 convolutions instead of using simple bilinear upsampling or the more expensive squeeze-and-excitation blocks. MLP has been successfully adopted in other segmentation architectures, like DeepLab and PointRend, and is efficient to compute on both CPU and GPU.
• Model size: With our new WebGL inference and the GPU-friendly model architecture, we were able to afford a larger model without sacrificing the real-time frame rate necessary for smooth video segmentation. We explored the width and the depth parameters using a neural architecture search.

HD segmentation model architecture.

In aggregate, these changes substantially improve the mean Intersection over Union (IoU) metric by 3%, resulting in less uncertainty and crisper boundaries around hair and fingers.

We have also released the accompanying model card for this segmentation model, which details our fairness evaluations. Our analysis shows that the model is consistent in its performance across the various regions, skin-tones, and genders, with only small deviations in IoU metrics.

Model		Resolution		Inference		IoU		Latency (ms)
CPU segmenter		256×144		Wasm SIMD		94.0%		8.7
GPU segmenter		512×288		WebGL		96.9%		4.3

Comparison of the previous segmentation model vs. the new HD segmentation model on a Macbook Pro (2018).

Accelerating Web ML with WebGL
One common challenge for web-based inference is that web technologies can incur a performance penalty when compared to apps running natively on-device. For GPUs, this penalty is substantial, only achieving around 25% of native OpenGL performance. This is because WebGL, the current GPU standard for Web-based inference, was primarily designed for image rendering, not arbitrary ML workloads. In particular, WebGL does not include compute shaders, which allow for general purpose computation and enable ML workloads in mobile and native apps.

To overcome this challenge, we accelerated low-level neural network kernels with fragment shaders that typically compute the output properties of a pixel like color and depth, and then applied novel optimizations inspired by the graphics community. As ML workloads on GPUs are often bound by memory bandwidth rather than compute, we focused on rendering techniques that would improve the memory access, such as Multiple Render Targets (MRT).

MRT is a feature in modern GPUs that allows rendering images to multiple output textures (OpenGL objects that represent images) at once. While MRT was originally designed to support advanced graphics rendering such as deferred shading, we found that we could leverage this feature to drastically reduce the memory bandwidth usage of our fragment shader implementations for critical operations, like convolutions and fully connected layers. We do so by treating intermediate tensors as multiple OpenGL textures.

In the figure below, we show an example of intermediate tensors having four underlying GL textures each. With MRT, the number of GPU threads, and thus effectively the number of memory requests for weights, is reduced by a factor of four and saves memory bandwidth usage. Although this introduces considerable complexities in the code, it helps us reach over 90% of native OpenGL performance, closing the gap with native applications.

Left: A classic implementation of Conv2D with 1-to-1 correspondence of tensor and an OpenGL texture. Red, yellow, green, and blue boxes denote different locations in a single texture each for intermediate tensor A and B. Right: Our implementation of Conv2D with MRT where intermediate tensors A and B are realized with a set of 4 GL textures each, depicted as red, yellow, green, and blue boxes. Note that this reduces the request count for weights by 4x.

Conclusion
We have made rapid strides in improving the quality of real-time segmentation models by leveraging the GPU on mid-tier and high-end devices for use with Google Meet. We look forward to the possibilities that will be enabled by upcoming technologies like WebGPU, which bring compute shaders to the web. Beyond GPU inference, we’re also working on improving the segmentation quality for lower powered devices with quantized inference via XNNPACK WebAssembly.

Acknowledgements
Special thanks to those on the Meet team and others who worked on this project, in particular Sebastian Jansson, Sami Kalliomäki, Rikard Lundmark, Stephan Reiter, Fabian Bergmark, Ben Wagner, Stefan Holmer, Dan Gunnarsson, Stéphane Hulaud, and to all our team members who made this possible: Siargey Pisarchyk, Raman Sarokin, Artsiom Ablavatski, Jamie Lin, Tyler Mullen, Gregory Karpiak, Andrei Kulik, Karthik Raveendran, Trent Tolley, and Matthias Grundmann.

Posted by Aparna Taneja, Software Engineer, and Milind Tambe, Principal Scientist, Google Research, India Research Lab

The widespread availability of mobile phones has enabled non-profits to deliver critical health information to their beneficiaries in a timely manner. While advanced applications on smartphones allow for richer multimedia content and two-way communication between beneficiaries and health coaches, simpler text and voice messaging services can be effective in disseminating information to large communities, particularly those that are underserved with limited access to information and smartphones. ARMMAN¹, one non-profit doing just this, is based in India with the mission of improving maternal and child health outcomes in underserved communities.

Overview of ARMMAN

One of the programs run by them is mMitra, which employs automated voice messaging to deliver timely preventive care information to expecting and new mothers during pregnancy and until one year after birth. These messages are tailored according to the gestational age of the beneficiary. Regular listenership to these messages has been shown to have a high correlation with improved behavioral and health outcomes, such as a 17% increase in infants with tripled birth weight at end of year and a 36% increase in women knowing the importance of taking iron tablets.

However, a key challenge ARMMAN faced was that about 40% of women gradually stopped engaging with the program. While it’s possible to mitigate this with live service calls to women to explain the advantage of listening to the messages, it is infeasible to call all the low listeners in the program because of limited support staff — this highlights the importance of effectively prioritizing who receives such service calls.

In “Field Study in Deploying Restless Multi-Armed Bandits: Assisting Non-Profits in Improving Maternal and Child Health”, published in AAAI 2022, we describe an ML-based solution that uses historical data from the NGO to predict which beneficiaries will benefit most from service calls. We address the challenges that come with a large-scale real world deployment of such a system and show the usefulness of deploying this model in a real study involving over 23,000 participants. The model showed an increase in listenership of 30% compared to the current standard of care group.

Background
We model this resource optimization problem using restless multi-armed bandits (RMABs), which have been well studied for application to such problems in a myriad of domains, including healthcare. An RMAB consists of n arms where each arm (representing a beneficiary) is associated with a two-state Markov decision process (MDP). Each MDP is modeled as a two-state (good or bad state, where the good state corresponds to high listenership in the previous week), two-action (corresponding to whether the beneficiary was chosen to receive a service call or not) problem. Further, each MDP has an associated reward function (i.e., the reward accumulated at a given state and action) and a transition function indicating the probability of moving from one state to the next under a given action, under the Markov condition that the next state depends only on the previous state and the action taken on that arm in that time step. The term restless indicates that all arms can change state irrespective of the action.

State of a beneficiary may transition from good (high engagement) to bad (low engagement) with example passive and active transition probabilities shown in the transition matrix.

Model Development
Finally, the RMAB problem is modeled such that at any time step, given n total arms, which k arms should be acted on (i.e., chosen to receive a service call), to maximize reward (engagement with the program).

The probability of transitioning from one state to another with (active probability) or without (passive probability) receiving a service call are therefore the underlying model parameters that are critical to solving the above optimization. To estimate these parameters, we use the demographic data of the beneficiaries collected at time of enrolment by the NGO, such as age, income, education, number of children, etc., as well as past listenership data, all in-line with the NGO’s data privacy standards (more below).

However, the limited volume of service calls limits the data corresponding to receiving a service call. To mitigate this, we use clustering techniques to learn from the collective observations of beneficiaries within a cluster and enable overcoming the challenge of limited samples per individual beneficiary.

In particular, we perform clustering on listenership behaviors, and then compute a mapping from the demographic features to each cluster.

Clustering on past listenership data reveals clusters with beneficiaries that behave similarly. We then infer a mapping from demographic features to clusters.

This mapping is useful because when a new beneficiary is enrolled, we only have access to their demographic information and have no knowledge of their listenership patterns, since they haven’t had a chance to listen yet. Using the mapping, we can infer transition probabilities for any new beneficiary that enrolls into the system.

We used several qualitative and quantitative metrics to infer the optimal set of of clusters and explored different combinations of training data (demographic features only, features plus passive probabilities, features plus all probabilities, passive probabilities only) to achieve the most meaningful clusters, that are representative of the underlying data distribution and have a low variance in individual cluster sizes.

Comparison of passive transition probabilities obtained from different clustering methods with number of clusters s = 20 (red dots) and 40 (green dots), using ground truth passive transition probabilities (blue dots). Clustering based on features+passive probabilities (PPF) captures more distinct beneficiary behaviors across the probability space.

Clustering has the added advantage of reducing computational cost for resource-limited NGOs, as the optimization needs to be solved at a cluster level rather than an individual level. Finally, solving RMAB’s is known to be P-space hard, so we choose to solve the optimization using the popular Whittle index approach, which ultimately provides a ranking of beneficiaries based on their likely benefit of receiving a service call.

Results
We evaluated the model in a real world study consisting of approximately 23,000 beneficiaries who were divided into three groups: the current standard of care (CSOC) group, the “round robin” (RR) group, and the RMAB group. The beneficiaries in the CSOC group follow the original standard of care, where there are no NGO initiated service calls. The RR group represents the scenario where the NGO often conducts service calls using some systematic set order — the idea here is to have an easily executable policy that services enough of a cross-section of beneficiaries and can be scaled up or down per week based on available resources (this is the approach used by the NGO in this particular case, but the approach may vary for different NGOs). The RMAB group receives service calls as predicted by the RMAB model. All the beneficiaries across the three groups continue to receive the automated voice messages independent of the service calls.

Distributions of clusters picked for service calls by RMAB and RR in week 1 (left) and 2 (right) are significantly different. RMAB is very strategic in picking only a few clusters with a promising probability of success (blue is high and red is low), RR displays no such strategic selection.

At the end of seven weeks, RMAB-based service calls resulted in the highest (and statistically significant) reduction in cumulative engagement drops (32%) compared to the CSOC group.

The plot shows cumulative engagement drops prevented compared to the control group.

	RMAB vs CSOC	RR vs CSOC	RMAB vs RR
% reduction in cumulative engagement drops	32.0%	5.2%	28.3%
p-value	0.044	0.740	0.098

Ethical Considerations
An ethics board at the NGO reviewed the study. We took significant measures to ensure participant consent is understood and recorded in a language of the community’s choice at each stage of the program. Data stewardship resides in the hands of the NGO, and only the NGO is allowed to share data. The code will soon be available publicly. The pipeline only uses anonymized data and no personally identifiable information (PII) is made available to the models. Sensitive data, such as caste, religion, etc., are not collected by ARMMAN for mMitra. Therefore, in pursuit of ensuring fairness of the model, we worked with public health and field experts to ensure other indicators of socioeconomic status were measured and adequately evaluated as shown below.

Distribution of highest education received (top) and monthly family income in Indian Rupees (bottom) across a cohort that received service calls compared to the whole population.

The proportion of beneficiaries that received a live service call within each income bracket reasonably matches the proportion in the overall population. However, differences are observed in lower income categories, where the RMAB model favors beneficiaries with lower income and beneficiaries with no formal education. Lastly, domain experts at ARMMAN have been deeply involved in the development and testing of this system and have provided continuous input and oversight in data interpretation, data consumption, and model design.

Conclusions
After thorough testing, the NGO has currently deployed this system for scheduling of service calls on a weekly basis. We are hopeful that this will pave the way for more deployments of ML algorithms for social impact in partnerships with non-profits in service of populations that have so far benefited less from ML. This work was also featured in Google for India 2021.

Acknowledgements
This work is part of our AI for Social Good efforts and was led by Google Research, India. Thanks to all our collaborators at ARMMAN, Google Research India, Google.org, and University Relations: Aparna Hegde, Neha Madhiwalla, Suresh Chaudhary, Aditya Mate, Lovish Madaan, Shresth Verma, Gargi Singh, Divy Thakkar.

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¹ARMMAN runs multiple programs to provide preventive care information to women through pregnancy and infancy enabling them to seek care, as well as programs to train and support health workers for timely detection and management of high-risk conditions. ^↩

Posted by Yilin Wang, Staff Software Engineer, YouTube and Feng Yang, Senior Staff Software Engineer, Google Research

Online video sharing platforms, like YouTube, need to understand perceptual video quality (i.e., a user’s subjective perception of video quality) in order to better optimize and improve user experience. Video quality assessment (VQA) attempts to build a bridge between video signals and perceptual quality by using objective mathematical models to approximate the subjective opinions of users. Traditional video quality metrics, like peak signal-to-noise ratio (PSNR) and Video Multi-Method Assessment Fusion (VMAF), are reference-based and focus on the relative difference between the target and reference videos. Such metrics, which work best on professionally generated content (e.g., movies), assume the reference video is of pristine quality and that one can induce the target video’s absolute quality from the relative difference.

However, the majority of the videos that are uploaded on YouTube are user-generated content (UGC), which bring new challenges due to their remarkably high variability in video content and original quality. Most UGC uploads are non-pristine and the same amount of relative difference could imply very different perceptual quality impacts. For example, people tend to be less sensitive to the distortions of poor quality uploads than of high quality uploads. Thus, reference-based quality scores become inaccurate and inconsistent when used for UGC cases. Additionally, despite the high volume of UGC, there are currently limited UGC video quality assessment (UGC-VQA) datasets with quality labels. Existing UGC-VQA datasets are either small in size (e.g., LIVE-Qualcomm has 208 samples captured from 54 unique scenes), compared with datasets with millions of samples for classification and recognition (e.g., ImageNet and YouTube-8M), or don’t have enough content variability (sampling without considering content information, like LIVE-VQC and KoNViD-1k).

In “Rich Features for Perceptual Quality Assessment of UGC Videos“, published at CVPR 2021, we describe how we attempt to solve the UGC quality assessment problem by building a Universal Video Quality (UVQ) model that resembles a subjective quality assessment. The UVQ model uses subnetworks to analyze UGC quality from high-level semantic information to low-level pixel distortions, and provides a reliable quality score with rationale (leveraging comprehensive and interpretable quality labels). Moreover, to advance UGC-VQA and compression research, we enhance the open-sourced YouTube-UGC dataset, which contains 1.5K representative UGC samples from millions of UGC videos (distributed under the Creative Commons license) on YouTube. The updated dataset contains ground-truth labels for both original videos and corresponding transcoded versions, enabling us to better understand the relationship between video content and its perceptual quality.

Subjective Video Quality Assessment
To understand perceptual video quality, we leverage an internal crowd-sourcing platform to collect mean opinion scores (MOS) with a scale of 1–5, where 1 is the lowest quality and 5 is the highest quality, for no-reference use cases. We collect ground-truth labels from the YouTube-UGC dataset and categorize UGC factors that affect quality perception into three high-level categories: (1) content, (2) distortions, and (3) compression. For example, a video with no meaningful content won’t receive a high quality MOS. Also, distortions introduced during the video production phase and video compression artifacts introduced by third-party platforms, e.g., transcoding or transmission, will degrade the overall quality.

MOS= 2.052	MOS= 4.457

Left: A video with no meaningful content won’t receive a high quality MOS. Right: A video displaying intense sports shows a higher MOS.

MOS= 1.242	MOS= 4.522

Left: A blurry gaming video gets a very low quality MOS. Right: A video with professional rendering (high contrast and sharp edges, usually introduced in the video production phase) shows a high quality MOS.

MOS= 2.372	MOS= 4.646

Left: A heavily compressed video receives a low quality MOS. Right: a video without compression artifacts shows a high quality MOS.

We demonstrate that the left gaming video in the second row of the figure above has the lowest MOS (1.2), even lower than the video with no meaningful content. A possible explanation is that viewers may have higher video quality expectations for videos that have a clear narrative structure, like gaming videos, and the blur artifacts significantly reduce the perceptual quality of the video.

UVQ Model Framework
A common method for evaluating video quality is to design sophisticated features, and then map these features to a MOS. However, designing useful handcrafted features is difficult and time-consuming, even for domain experts. Also, the most useful existing handcrafted features were summarized from limited samples, which may not perform well on broader UGC cases. In contrast, machine learning is becoming more prominent in UGC-VQA because it can automatically learn features from large-scale samples.

A straightforward approach is to train a model from scratch on existing UGC quality datasets. However, this may not be feasible as there are limited quality UGC datasets. To overcome this limitation, we apply a self-supervised learning step to the UVQ model during training. This self-supervised step enables us to learn comprehensive quality-related features, without ground-truth MOS, from millions of raw videos.

Following the quality-related categories summarized from the subjective VQA, we develop the UVQ model with four novel subnetworks. The first three subnetworks, which we call ContentNet, DistortionNet and CompressionNet, are used to extract quality features (i.e., content, distortion and compression), and the fourth subnetwork, called AggregationNet, maps the extracted features to generate a single quality score. ContentNet is trained in a supervised learning fashion with UGC-specific content labels that are generated by the YouTube-8M model. DistortionNet is trained to detect common distortions, e.g., Gaussian blur and white noise of the original frame. CompressionNet focuses on video compression artifacts, whose training data are videos compressed with different bitrates. CompressionNet is trained using two compressed variants of the same content that are fed into the model to predict corresponding compression levels (with a higher score for more noticeable compression artifacts), with the implicit assumption that the higher bitrate version has a lower compression level.

The ContentNet, DistortionNet and CompressionNet subnetworks are trained on large-scale samples without ground-truth quality scores. Since video resolution is also an important quality factor, the resolution-sensitive subnetworks (CompressionNet and DistortionNet) are patch-based (i.e., each input frame is divided into multiple disjointed patches that are processed separately), which makes it possible to capture all detail on native resolution without downscaling. The three subnetworks extract quality features that are then concatenated by the fourth subnetwork, AggregationNet, to predict quality scores with domain ground-truth MOS from YouTube-UGC.

The UVQ training framework.

Analyzing Video Quality with UVQ
After building the UVQ model, we use it to analyze the video quality of samples pulled from YouTube-UGC and demonstrate that its subnetworks can provide a single quality score along with high-level quality indicators that can help us understand quality issues. For example, DistortionNet detects multiple visual artifacts, e.g., jitter and lens blur, for the middle video below, and CompressionNet detects that the bottom video has been heavily compressed.

ContentNet assigns content labels with corresponding probabilities in parentheses, i.e., car (0.58), vehicle (0.42), sports car (0.32), motorsports (0.18), racing (0.11).

DistortionNet detects and categorizes multiple visual distortions with corresponding probabilities in parentheses, i.e., jitter (0.112), color quantization (0.111), lens blur (0.108), denoise (0.107).

CompressionNet detects a high compression level of 0.892 for the video above.

Additionally, UVQ can provide patch-based feedback to locate quality issues. Below, UVQ reports that the quality of the first patch (patch at time t = 1) is good with a low compression level. However, the model identifies heavy compression artifacts in the next patch (patch at time t = 2).

Patch at time t = 1	Patch at time t = 2
Compression level = 0.000	Compression level = 0.904

UVQ detects a sudden quality degradation (high compression level) for a local patch.

In practice, UVQ can generate a video diagnostic report that includes a content description (e.g., strategy video game), distortion analysis (e.g., the video is blurry or pixelated) and compression level (e.g., low or high compression). Below, UVQ reports that the content quality, looking at individual features, is good, but the compression and distortion quality is low. When combining all three features, the overall quality is medium-low. We see that these findings are close to the rationale summarized by internal user experts, demonstrating that UVQ can reason through quality assessments, while providing a single quality score.

UVQ diagnostic report. ContentNet (CT): Video game, strategy video game, World of Warcraft, etc. DistortionNet (DT): multiplicative noise, Gaussian blur, color saturation, pixelate, etc. CompressionNet (CP): 0.559 (medium-high compression). Predicted quality score in [1, 5]: (CT, DT, CP) = (3.901, 3.216, 3.151), (CT+DT+CP) = 3.149 (medium-low quality).

Conclusion
We present the UVQ model, which generates a report with quality scores and insights that can be used to interpret UGC video perceptual quality. UVQ learns comprehensive quality related features from millions of UGC videos and provides a consistent view of quality interpretation for both no-reference and reference cases. To learn more, read our paper or visit our website to see YT-UGC videos and their subjective quality data. We also hope that the enhanced YouTube-UGC dataset enables more research in this space.

Acknowledgements
This work was possible through a collaboration spanning several Google teams. Key contributors include: Balu Adsumilli, Neil Birkbeck, Joong Gon Yim from YouTube and Junjie Ke, Hossein Talebi, Peyman Milanfar from Google Research. Thanks to Ross Wolf, Jayaprasanna Jayaraman, Carena Church, and Jessie Lin for their contributions.