Posts in category: Google AI

Posted by Fitsum Reda and Janne Kontkanen, Google Research

Frame interpolation is the process of synthesizing in-between images from a given set of images. The technique is often used for temporal up-sampling to increase the refresh rate of videos or to create slow motion effects. Nowadays, with digital cameras and smartphones, we often take several photos within a few seconds to capture the best picture. Interpolating between these “near-duplicate” photos can lead to engaging videos that reveal scene motion, often delivering an even more pleasing sense of the moment than the original photos.

Frame interpolation between consecutive video frames, which often have small motion, has been studied extensively. Unlike videos, however, the temporal spacing between near-duplicate photos can be several seconds, with commensurately large in-between motion, which is a major failing point of existing frame interpolation methods. Recent methods attempt to handle large motion by training on datasets with extreme motion, albeit with limited effectiveness on smaller motions.

In “FILM: Frame Interpolation for Large Motion”, published at ECCV 2022, we present a method to create high quality slow-motion videos from near-duplicate photos. FILM is a new neural network architecture that achieves state-of-the-art results in large motion, while also handling smaller motions well.

FILM interpolating between two near-duplicate photos to create a slow motion video.

FILM Model Overview

The FILM model takes two images as input and outputs a middle image. At inference time, we recursively invoke the model to output in-between images. FILM has three components: (1) A feature extractor that summarizes each input image with deep multi-scale (pyramid) features; (2) a bi-directional motion estimator that computes pixel-wise motion (i.e., flows) at each pyramid level; and (3) a fusion module that outputs the final interpolated image. We train FILM on regular video frame triplets, with the middle frame serving as the ground-truth for supervision.

A standard feature pyramid extraction on two input images. Features are processed at each level by a series of convolutions, which are then downsampled to half the spatial resolution and passed as input to the deeper level.

Scale-Agnostic Feature Extraction

Large motion is typically handled with hierarchical motion estimation using multi-resolution feature pyramids (shown above). However, this method struggles with small and fast-moving objects because they can disappear at the deepest pyramid levels. In addition, there are far fewer available pixels to derive supervision at the deepest level.

To overcome these limitations, we adopt a feature extractor that shares weights across scales to create a “scale-agnostic” feature pyramid. This feature extractor (1) allows the use of a shared motion estimator across pyramid levels (next section) by equating large motion at shallow levels with small motion at deeper levels, and (2) creates a compact network with fewer weights.

Specifically, given two input images, we first create an image pyramid by successively downsampling each image. Next, we use a shared U-Net convolutional encoder to extract a smaller feature pyramid from each image pyramid level (columns in the figure below). As the third and final step, we construct a scale-agnostic feature pyramid by horizontally concatenating features from different convolution layers that have the same spatial dimensions. Note that from the third level onwards, the feature stack is constructed with the same set of shared convolution weights (shown in the same color). This ensures that all features are similar, which allows us to continue to share weights in the subsequent motion estimator. The figure below depicts this process using four pyramid levels, but in practice, we use seven.

Bi-directional Flow Estimation

After feature extraction, FILM performs pyramid-based residual flow estimation to compute the flows from the yet-to-be-predicted middle image to the two inputs. The flow estimation is done once for each input, starting from the deepest level, using a stack of convolutions. We estimate the flow at a given level by adding a residual correction to the upsampled estimate from the next deeper level. This approach takes the following as its input: (1) the features from the first input at that level, and (2) the features of the second input after it is warped with the upsampled estimate. The same convolution weights are shared across all levels, except for the two finest levels.

Shared weights allow the interpretation of small motions at deeper levels to be the same as large motions at shallow levels, boosting the number of pixels available for large motion supervision. Additionally, shared weights not only enable the training of powerful models that may reach a higher peak signal-to-noise ratio (PSNR), but are also needed to enable models to fit into GPU memory for practical applications.

Fusion and Frame Generation

Once the bi-directional flows are estimated, we warp the two feature pyramids into alignment. We obtain a concatenated feature pyramid by stacking, at each pyramid level, the two aligned feature maps, the bi-directional flows and the input images. Finally, a U-Net decoder synthesizes the interpolated output image from the aligned and stacked feature pyramid.

FILM Architecture. FEATURE EXTRACTION: we extract scale-agnostic features. The features with matching colors are extracted using shared weights. FLOW ESTIMATION: we compute bi-directional flows using shared weights across the deeper pyramid levels and warp the features into alignment. FUSION: A U-Net decoder outputs the final interpolated frame.

Loss Functions

During training, we supervise FILM by combining three losses. First, we use the absolute L1 difference between the predicted and ground-truth frames to capture the motion between input images. However, this produces blurry images when used alone. Second, we use perceptual loss to improve image fidelity. This minimizes the L1 difference between the ImageNet pre-trained VGG-19 features extracted from the predicted and ground truth frames. Third, we use Style loss to minimize the L2 difference between the Gram matrix of the ImageNet pre-trained VGG-19 features. The Style loss enables the network to produce sharp images and realistic inpaintings of large pre-occluded regions. Finally, the losses are combined with weights empirically selected such that each loss contributes equally to the total loss.

Shown below, the combined loss greatly improves sharpness and image fidelity when compared to training FILM with L1 loss and VGG losses. The combined loss maintains the sharpness of the tree leaves.

FILM’s combined loss functions. L1 loss (left), L1 plus VGG loss (middle), and Style loss (right), showing significant sharpness improvements (green box).

Image and Video Results

We evaluate FILM on an internal near-duplicate photos dataset that exhibits large scene motion. Additionally, we compare FILM to recent frame interpolation methods: SoftSplat and ABME. FILM performs favorably when interpolating across large motion. Even in the presence of motion as large as 100 pixels, FILM generates sharp images consistent with the inputs.

Frame interpolation with SoftSplat (left), ABME (middle) and FILM (right) showing favorable image quality and temporal consistency.

Large motion interpolation. Top: 64x slow motion video. Bottom (left to right): The two input images blended, SoftSplat interpolation, ABME interpolation, and FILM interpolation. FILM captures the dog’s face while maintaining the background details.

Conclusion

We introduce FILM, a large motion frame interpolation neural network. At its core, FILM adopts a scale-agnostic feature pyramid that shares weights across scales, which allows us to build a “scale-agnostic” bi-directional motion estimator that learns from frames with normal motion and generalizes well to frames with large motion. To handle wide disocclusions caused by large scene motion, we supervise FILM by matching the Gram matrix of ImageNet pre-trained VGG-19 features, which results in realistic inpainting and crisp images. FILM performs favorably on large motion, while also handling small and medium motions well, and generates temporally smooth high quality videos.

Try It Out Yourself

You can try out FILM on your photos using the source code, which is now publicly available.

Acknowledgements

We would like to thank Eric Tabellion, Deqing Sun, Caroline Pantofaru, Brian Curless for their contributions. We thank Marc Comino Trinidad for his contributions on the scale-agnostic feature extractor, Orly Liba and Charles Herrmann for feedback on the text, Jamie Aspinall for the imagery in the paper, Dominik Kaeser, Yael Pritch, Michael Nechyba, William T. Freeman, David Salesin, Catherine Wah, and Ira Kemelmacher-Shlizerman for support. Thanks to Tom Small for creating the animated diagram in this post.

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Posted by Srivatsan Krishnan, Student Researcher, and Aleksandra Faust, Senior Staff Research Scientist, Google Research, Brain Team

Deep reinforcement learning (RL) continues to make great strides in solving real-world sequential decision-making problems such as balloon navigation, nuclear physics, robotics, and games. Despite its promise, one of its limiting factors is long training times. While the current approach to speed up RL training on complex and difficult tasks leverages distributed training scaling up to hundreds or even thousands of computing nodes, it still requires the use of significant hardware resources which makes RL training expensive, while increasing its environmental impact. However, recent work [1, 2] indicates that performance optimizations on existing hardware can reduce the carbon footprint (i.e., total greenhouse gas emissions) of training and inference.

RL can also benefit from similar system optimization techniques that can reduce training time, improve hardware utilization and reduce carbon dioxide (CO₂) emissions. One such technique is quantization, a process that converts full-precision floating point (FP32) numbers to lower precision (int8) numbers and then performs computation using the lower precision numbers. Quantization can save memory storage cost and bandwidth for faster and more energy-efficient computation. Quantization has been successfully applied to supervised learning to enable edge deployments of machine learning (ML) models and achieve faster training. However, there remains an opportunity to apply quantization to RL training.

To that end, we present “QuaRL: Quantization for Fast and Environmentally SustainableReinforcement Learning”, published in the Transactions of Machine Learning Research journal, which introduces a new paradigm called ActorQ that applies quantization to speed up RL training by 1.5-5.4x while maintaining performance. Additionally, we demonstrate that compared to training in full-precision, the carbon footprint is also significantly reduced by a factor of 1.9-3.8x.

Applying Quantization to RL Training

In traditional RL training, a learner policy is applied to an actor, which uses the policy to explore the environment and collect data samples. The samples collected by the actor are then used by the learner to continuously refine the initial policy. Periodically, the policy trained on the learner side is used to update the actor’s policy. To apply quantization to RL training, we develop the ActorQ paradigm. ActorQ performs the same sequence described above, with one key difference being that the policy update from learner to actors is quantized, and the actor explores the environment using the int8 quantized policy to collect samples.

Applying quantization to RL training in this fashion has two key benefits. First, it reduces the memory footprint of the policy. For the same peak bandwidth, less data is transferred between learners and actors, which reduces the communication cost for policy updates from learners to actors. Second, the actors perform inference on the quantized policy to generate actions for a given environment state. The quantized inference process is much faster when compared to performing inference in full precision.

An overview of traditional RL training (left) and ActorQ RL training (right).

In ActorQ, we use the ACME distributed RL framework. The quantizer block performs uniform quantization that converts the FP32 policy to int8. The actor performs inference using optimized int8 computations. Though we use uniform quantization when designing the quantizer block, we believe that other quantization techniques can replace uniform quantization and produce similar results. The samples collected by the actors are used by the learner to train a neural network policy. Periodically the learned policy is quantized by the quantizer block and broadcasted to the actors.

Quantization Improves RL Training Time and Performance

We evaluate ActorQ in a range of environments, including the Deepmind Control Suite and the OpenAI Gym. We demonstrate the speed-up and improved performance of D4PG and DQN. We chose D4PG as it was the best learning algorithm in ACME for Deepmind Control Suite tasks, and DQN is a widely used and standard RL algorithm.

We observe a significant speedup (between 1.5x and 5.41x) in training RL policies. More importantly, performance is maintained even when actors perform int8 quantized inference. The figures below demonstrate this for the D4PG and DQN agents for Deepmind Control Suite and OpenAI Gym tasks.

A comparison of RL training using the FP32 policy (q=32) and the quantized int8 policy (q=8) for D4PG agents on various Deepmind Control Suite tasks. Quantization achieves speed-ups of 1.5x to 3.06x.

A comparison of RL training using the FP32 policy (q=32) and the quantized int8 policy (q=8) for DQN agents in the OpenAI Gym environment. Quantization achieves a speed-up of 2.2x to 5.41x.

Quantization Reduces Carbon Emission

Applying quantization in RL using ActorQ improves training time without affecting performance. The direct consequence of using the hardware more efficiently is a smaller carbon footprint. We measure the carbon footprint improvement by taking the ratio of carbon emission when using the FP32 policy during training over the carbon emission when using the int8 policy during training.

In order to measure the carbon emission for the RL training experiment, we use the experiment-impact-tracker proposed in prior work. We instrument the ActorQ system with carbon monitor APIs to measure the energy and carbon emissions for each training experiment.

Compared to the carbon emission when running in full precision (FP32), we observe that the quantization of policies reduces the carbon emissions anywhere from 1.9x to 3.76x, depending on the task. As RL systems are scaled to run on thousands of distributed hardware cores and accelerators, we believe that the absolute carbon reduction (measured in kilograms of CO₂) can be quite significant.

Carbon emission comparison between training using a FP32 policy and an int8 policy. The X-axis scale is normalized to the carbon emissions of the FP32 policy. Shown by the red bars greater than 1, ActorQ reduces carbon emissions.

Conclusion and Future Directions

We introduce ActorQ, a novel paradigm that applies quantization to RL training and achieves speed-up improvements of 1.5-5.4x while maintaining performance. Additionally, we demonstrate that ActorQ can reduce RL training’s carbon footprint by a factor of 1.9-3.8x compared to training in full-precision without quantization.

ActorQ demonstrates that quantization can be effectively applied to many aspects of RL, from obtaining high-quality and efficient quantized policies to reducing training times and carbon emissions. As RL continues to make great strides in solving real-world problems, we believe that making RL training sustainable will be critical for adoption. As we scale RL training to thousands of cores and GPUs, even a 50% improvement (as we have experimentally demonstrated) will generate significant savings in absolute dollar cost, energy, and carbon emissions. Our work is the first step toward applying quantization to RL training to achieve efficient and environmentally sustainable training.

While our design of the quantizer in ActorQ relied on simple uniform quantization, we believe that other forms of quantization, compression and sparsity can be applied (e.g., distillation, sparsification, etc.). We hope that future work will consider applying more aggressive quantization and compression methods, which may yield additional benefits to the performance and accuracy tradeoff obtained by the trained RL policies.

Acknowledgments

We would like to thank our co-authors Max Lam, Sharad Chitlangia, Zishen Wan, and Vijay Janapa Reddi (Harvard University), and Gabriel Barth-Maron (DeepMind), for their contribution to this work. We also thank the Google Cloud team for providing research credits to seed this work.

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Posted by Jeremy Maitin-Shepard and Laramie Leavitt, Software Engineers, Connectomics at Google

Many exciting contemporary applications of computer science and machine learning (ML) manipulate multidimensional datasets that span a single large coordinate system, for example, weather modeling from atmospheric measurements over a spatial grid or medical imaging predictions from multi-channel image intensity values in a 2d or 3d scan. In these settings, even a single dataset may require terabytes or petabytes of data storage. Such datasets are also challenging to work with as users may read and write data at irregular intervals and varying scales, and are often interested in performing analyses using numerous machines working in parallel.

Today we are introducing TensorStore, an open-source C++ and Python software library designed for storage and manipulation of n-dimensional data that:

• Provides a uniform API for reading and writing multiple array formats, including zarr and N5.
• Natively supports multiple storage systems, including Google Cloud Storage, local and network filesystems, HTTP servers, and in-memory storage.
• Supports read/writeback caching and transactions, with strong atomicity, isolation, consistency, and durability (ACID) guarantees.
• Supports safe, efficient access from multiple processes and machines via optimistic concurrency.
• Offers an asynchronous API to enable high-throughput access even to high-latency remote storage.
• Provides advanced, fully composable indexing operations and virtual views.

TensorStore has already been used to solve key engineering challenges in scientific computing (e.g., management and processing of large datasets in neuroscience, such as peta-scale 3d electron microscopy data and “4d” videos of neuronal activity). TensorStore has also been used in the creation of large-scale machine learning models such as PaLM by addressing the problem of managing model parameters (checkpoints) during distributed training.

Familiar API for Data Access and Manipulation

TensorStore provides a simple Python API for loading and manipulating large array data. In the following example, we create a TensorStore object that represents a 56 trillion voxel 3d image of a fly brain and access a small 100×100 patch of the data as a NumPy array:

>>> import tensorstore as ts
>>> import numpy as np

# Create a TensorStore object to work with fly brain data.
>>> dataset = ts.open({
...     'driver':
...         'neuroglancer_precomputed',
...     'kvstore':
...         'gs://neuroglancer-janelia-flyem-hemibrain/' + 
...         'v1.1/segmentation/',
... }).result()

# Create a 3-d view (remove singleton 'channel' dimension):
>>> dataset_3d = dataset[ts.d['channel'][0]]
>>> dataset_3d.domain
{ "x": [0, 34432), "y": [0, 39552), "z": [0, 41408) }

# Convert a 100x100x1 slice of the data to a numpy ndarray
>>> slice = np.array(dataset_3d[15000:15100, 15000:15100, 20000])

Crucially, no actual data is accessed or stored in memory until the specific 100×100 slice is requested; hence arbitrarily large underlying datasets can be loaded and manipulated without having to store the entire dataset in memory, using indexing and manipulation syntax largely identical to standard NumPy operations. TensorStore also provides extensive support for advanced indexing features, including transforms, alignment, broadcasting, and virtual views (data type conversion, downsampling, lazily on-the-fly generated arrays).

The following example demonstrates how TensorStore can be used to create a zarr array, and how its asynchronous API enables higher throughput:

>>> import tensorstore as ts
>>> import numpy as np

>>> # Create a zarr array on the local filesystem
>>> dataset = ts.open({
...     'driver': 'zarr',
...     'kvstore': 'file:///tmp/my_dataset/',
... },
... dtype=ts.uint32,
... chunk_layout=ts.ChunkLayout(chunk_shape=[256, 256, 1]),
... create=True,
... shape=[5000, 6000, 7000]).result()

>>> # Create two numpy arrays with example data to write.
>>> a = np.arange(100*200*300, dtype=np.uint32).reshape((100, 200, 300))
>>> b = np.arange(200*300*400, dtype=np.uint32).reshape((200, 300, 400))

>>> # Initiate two asynchronous writes, to be performed concurrently.
>>> future_a = dataset[1000:1100, 2000:2200, 3000:3300].write(a)
>>> future_b = dataset[3000:3200, 4000:4300, 5000:5400].write(b)

>>> # Wait for the asynchronous writes to complete
>>> future_a.result()
>>> future_b.result()

Safe and Performant Scaling

Processing and analyzing large numerical datasets requires significant computational resources. This is typically achieved through parallelization across numerous CPU or accelerator cores spread across many machines. Therefore a fundamental goal of TensorStore has been to enable parallel processing of individual datasets that is both safe (i.e., avoids corruption or inconsistencies arising from parallel access patterns) and high performance (i.e., reading and writing to TensorStore is not a bottleneck during computation). In fact, in a test within Google’s datacenters, we found nearly linear scaling of read and write performance as the number of CPUs was increased:

Read and write performance for a TensorStore dataset in zarr format residing on Google Cloud Storage (GCS) accessed concurrently using a variable number of single-core compute tasks in Google data centers. Both read and write performance scales nearly linearly with the number of compute tasks.

Performance is achieved by implementing core operations in C++, extensive use of multithreading for operations such as encoding/decoding and network I/O, and partitioning large datasets into much smaller units through chunking to enable efficiently reading and writing subsets of the entire dataset. TensorStore also provides configurable in-memory caching (which reduces slower storage system interactions for frequently accessed data) and an asynchronous API that enables a read or write operation to continue in the background while a program completes other work.

Safety of parallel operations when many machines are accessing the same dataset is achieved through the use of optimistic concurrency, which maintains compatibility with diverse underlying storage layers (including Cloud storage platforms, such as GCS, as well as local filesystems) without significantly impacting performance. TensorStore also provides strong ACID guarantees for all individual operations executing within a single runtime.

To make distributed computing with TensorStore compatible with many existing data processing workflows, we have also integrated TensorStore with parallel computing libraries such as Apache Beam (example code) and Dask (example code).

Use Case: Language Models

An exciting recent development in ML is the emergence of more advanced language models such as PaLM. These neural networks contain hundreds of billions of parameters and exhibit some surprising capabilities in natural language understanding and generation. These models also push the limits of computational infrastructure; in particular, training a language model such as PaLM requires thousands of TPUs working in parallel.

One challenge that arises during this training process is efficiently reading and writing the model parameters. Training is distributed across many separate machines, but parameters must be regularly saved to a single object (“checkpoint”) on a permanent storage system without slowing down the overall training process. Individual training jobs must also be able to read just the specific set of parameters they are concerned with in order to avoid the overhead that would be required to load the entire set of model parameters (which could be hundreds of gigabytes).

TensorStore has already been used to address these challenges. It has been applied to manage checkpoints associated with large-scale (“multipod”) models trained with JAX (code example) and has been integrated with frameworks such as T5X (code example) and Pathways. Model parallelism is used to partition the full set of parameters, which can occupy more than a terabyte of memory, over hundreds of TPUs. Checkpoints are stored in zarr format using TensorStore, with a chunk structure chosen to allow the partition for each TPU to be read and written independently in parallel.

When saving a checkpoint, each model parameter is written using TensorStore in zarr format using a chunk grid that further subdivides the grid used to partition the parameter over TPUs. The host machines write in parallel the zarr chunks for each of the partitions assigned to TPUs attached to that host. Using TensorStore’s asynchronous API, training proceeds even while the data is still being written to persistent storage. When resuming from a checkpoint, each host reads only the chunks that make up the partitions assigned to that host.

Use Case: 3D Brain Mapping

The field of synapse-resolution connectomics aims to map the wiring of animal and human brains at the detailed level of individual synaptic connections. This requires imaging the brain at extremely high resolution (nanometers) over fields of view of up to millimeters or more, which yields datasets that can span petabytes in size. In the future these datasets may extend to exabytes as scientists contemplate mapping entire mouse or primate brains. However, even current datasets pose significant challenges related to storage, manipulation, and processing; in particular, even a single brain sample may require millions of gigabytes with a coordinate system (pixel space) of hundreds of thousands pixels in each dimension.

We have used TensorStore to solve computational challenges associated with large-scale connectomic datasets. Specifically, TensorStore has managed some of the largest and most widely accessed connectomic datasets, with Google Cloud Storage as the underlying object storage system. For example, it has been applied to the human cortex “h01” dataset, which is a 3d nanometer-resolution image of human brain tissue. The raw imaging data is 1.4 petabytes (roughly 500,000 * 350,000 * 5,000 pixels large, and is further associated with additional content such as 3d segmentations and annotations that reside in the same coordinate system. The raw data is subdivided into individual chunks 128x128x16 pixels large and stored in the “Neuroglancer precomputed” format, which is optimized for web-based interactive viewing and can be easily manipulated from TensorStore.

A fly brain reconstruction for which the underlying data can be easily accessed and manipulated using TensorStore.

Getting Started

To get started using the TensorStore Python API, you can install the tensorstore PyPI package using:

pip install tensorstore

Refer to the tutorials and API documentation for usage details. For other installation options and for using the C++ API, refer to installation instructions.

Acknowledgements

Thanks to Tim Blakely, Viren Jain, Yash Katariya, Jan-Matthis Luckmann, Michał Januszewski, Peter Li, Adam Roberts, Brain Williams, and Hector Yee from Google Research, and Davis Bennet, Stuart Berg, Eric Perlman, Stephen Plaza, and Juan Nunez-Iglesias from the broader scientific community for valuable feedback on the design, early testing and debugging.

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Posted by Carlos Esteves and Ameesh Makadia, Research Scientists, Google Research

A long-standing problem in the intersection of computer vision and computer graphics, view synthesis is the task of creating new views of a scene from multiple pictures of that scene. This has received increased attention [1, 2, 3] since the introduction of neural radiance fields (NeRF). The problem is challenging because to accurately synthesize new views of a scene, a model needs to capture many types of information — its detailed 3D structure, materials, and illumination — from a small set of reference images.

In this post, we present recently published deep learning models for view synthesis. In “Light Field Neural Rendering” (LFNR), presented at CVPR 2022, we address the challenge of accurately reproducing view-dependent effects by using transformers that learn to combine reference pixel colors. Then in “Generalizable Patch-Based Neural Rendering” (GPNR), to be presented at ECCV 2022, we address the challenge of generalizing to unseen scenes by using a sequence of transformers with canonicalized positional encoding that can be trained on a set of scenes to synthesize views of new scenes. These models have some unique features. They perform image-based rendering, combining colors and features from the reference images to render novel views. They are purely transformer-based, operating on sets of image patches, and they leverage a 4D light field representation for positional encoding, which helps to model view-dependent effects.

We train deep learning models that are able to produce new views of a scene given a few images of it. These models are particularly effective when handling view-dependent effects like the refractions and translucency on the test tubes. This animation is compressed; see the original-quality renderings here. Source: Lab scene from the NeX/Shiny dataset.

Overview

The input to the models consists of a set of reference images and their camera parameters (focal length, position, and orientation in space), along with the coordinates of the target ray whose color we want to determine. To produce a new image, we start from the camera parameters of the input images, obtain the coordinates of the target rays (each corresponding to a pixel), and query the model for each.

Instead of processing each reference image completely, we look only at the regions that are likely to influence the target pixel. These regions are determined via epipolar geometry, which maps each target pixel to a line on each reference frame. For robustness, we take small regions around a number of points on the epipolar line, resulting in the set of patches that will actually be processed by the model. The transformers then act on this set of patches to obtain the color of the target pixel.

Transformers are especially useful in this setting since their self-attention mechanism naturally takes sets as inputs, and the attention weights themselves can be used to combine reference view colors and features to predict the output pixel colors. These transformers follow the architecture introduced in ViT.

To predict the color of one pixel, the models take a set of patches extracted around the epipolar line of each reference view. Image source: LLFF dataset.

Light Field Neural Rendering

In Light Field Neural Rendering (LFNR), we use a sequence of two transformers to map the set of patches to the target pixel color. The first transformer aggregates information along each epipolar line, and the second along each reference image. We can interpret the first transformer as finding potential correspondences of the target pixel on each reference frame, and the second as reasoning about occlusion and view-dependent effects, which are common challenges of image-based rendering.

LFNR uses a sequence of two transformers to map a set of patches extracted along epipolar lines to the target pixel color.

LFNR improved the state-of-the-art on the most popular view synthesis benchmarks (Blender and Real Forward-Facing scenes from NeRF and Shiny from NeX) with margins as large as 5dB peak signal-to-noise ratio (PSNR). This corresponds to a reduction of the pixel-wise error by a factor of 1.8x. We show qualitative results on challenging scenes from the Shiny dataset below:

LFNR reproduces challenging view-dependent effects like the rainbow and reflections on the CD, reflections, refractions and translucency on the bottles. This animation is compressed; see the original quality renderings here. Source: CD scene from the NeX/Shiny dataset.

Prior methods such as NeX and NeRF fail to reproduce view-dependent effects like the translucency and refractions in the test tubes on the Lab scene from the NeX/Shiny dataset. See also our video of this scene at the top of the post and the original quality outputs here.

Generalizing to New Scenes

One limitation of LFNR is that the first transformer collapses the information along each epipolar line independently for each reference image. This means that it decides which information to preserve based only on the output ray coordinates and patches from each reference image, which works well when training on a single scene (as most neural rendering methods do), but it does not generalize across scenes. Generalizable methods are important because they can be applied to new scenes without needing to retrain.

We overcome this limitation of LFNR in Generalizable Patch-Based Neural Rendering (GPNR). We add a transformer that runs before the other two and exchanges information between points at the same depth over all reference images. For example, this first transformer looks at the columns of the patches from the park bench shown above and can use cues like the flower that appears at corresponding depths in two views, which indicates a potential match. Another key idea of this work is to canonicalize the positional encoding based on the target ray, because to generalize across scenes, it is necessary to represent quantities in relative and not absolute frames of reference. The animation below shows an overview of the model.

GPNR consists of a sequence of three transformers that map a set of patches extracted along epipolar lines to a pixel color. Image patches are mapped via the linear projection layer to initial features (shown as blue and green boxes). Then those features are successively refined and aggregated by the model, resulting in the final feature/color represented by the gray rectangle. Park bench image source: LLFF dataset.

To evaluate the generalization performance, we train GPNR on a set of scenes and test it on new scenes. GPNR improved the state-of-the-art on several benchmarks (following IBRNet and MVSNeRF protocols) by 0.5–1.0 dB on average. On the IBRNet benchmark, GPNR outperforms the baselines while using only 11% of the training scenes. The results below show new views of unseen scenes rendered with no fine-tuning.

GPNR-generated views of held-out scenes, without any fine tuning. This animation is compressed; see the original quality renderings here. Source: IBRNet collected dataset.

Details of GPNR-generated views on held-out scenes from NeX/Shiny (left) and LLFF (right), without any fine tuning. GPNR reproduces more accurately the details on the leaf and the refractions through the lens when compared against IBRNet.

Future Work

One limitation of most neural rendering methods, including ours, is that they require camera poses for each input image. Poses are not easy to obtain and typically come from offline optimization methods that can be slow, limiting possible applications, such as those on mobile devices. Research on jointly learning view synthesis and input poses is a promising future direction. Another limitation of our models is that they are computationally expensive to train. There is an active line of research on faster transformers which might help improve our models’ efficiency. For the papers, more results, and open-source code, you can check out the projects pages for “Light Field Neural Rendering” and “Generalizable Patch-Based Neural Rendering“.

Potential Misuse

In our research, we aim to accurately reproduce an existing scene using images from that scene, so there is little room to generate fake or non-existing scenes. Our models assume static scenes, so synthesizing moving objects, such as people, will not work.

Acknowledgments

All the hard work was done by our amazing intern – Mohammed Suhail – a PhD student at UBC, in collaboration with Carlos Esteves and Ameesh Makadia from Google Research, and Leonid Sigal from UBC. We are thankful to Corinna Cortes for supporting and encouraging this project.

Our work is inspired by NeRF, which sparked the recent interest in view synthesis, and IBRNet, which first considered generalization to new scenes. Our light ray positional encoding is inspired by the seminal paper Light Field Rendering and our use of transformers follow ViT.

Video results are from scenes from LLFF, Shiny, and IBRNet collected datasets.

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Posted by Weicheng Kuo and Anelia Angelova, Research Scientists, Google Research, Brain Team

Natural language enables flexible descriptive queries about images. The interaction between text queries and images grounds linguistic meaning in the visual world, facilitating a better understanding of object relationships, human intentions towards objects, and interactions with the environment. The research community has studied object-level visual grounding through a range of tasks, including referring expression comprehension, text-based localization, and more broadly object detection, each of which require different skills in a model. For example, object detection seeks to find all objects from a predefined set of classes, which requires accurate localization and classification, while referring expression comprehension localizes an object from a referring text and often requires complex reasoning on prominent objects. At the intersection of the two is text-based localization, in which a simple category-based text query prompts the model to detect the objects of interest.

Due to their dissimilar task properties, referring expression comprehension, detection, and text-based localization are mostly studied through separate benchmarks with most models only dedicated to one task. As a result, existing models have not adequately synthesized information from the three tasks to achieve a more holistic visual and linguistic understanding. Referring expression comprehension models, for instance, are trained to predict one object per image, and often struggle to localize multiple objects, reject negative queries, or detect novel categories. In addition, detection models are unable to process text inputs, and text-based localization models often struggle to process complex queries that refer to one object instance, such as “Left half sandwich.” Lastly, none of the models can generalize sufficiently well beyond their training data and categories.

To address these limitations, we are presenting “FindIt: Generalized Localization with Natural Language Queries” at ECCV 2022. Here we propose a unified, general-purpose and multitask visual grounding model, called FindIt, that can flexibly answer different types of grounding and detection queries. Key to this architecture is a multi-level cross-modality fusion module that can perform complex reasoning for referring expression comprehension and simultaneously recognize small and challenging objects for text-based localization and detection. In addition, we discover that a standard object detector and detection losses are sufficient and surprisingly effective for all three tasks without the need for task-specific design and losses common in existing works. FindIt is simple, efficient, and outperforms alternative state-of-the-art models on the referring expression comprehension and text-based localization benchmarks, while being competitive on the detection benchmark.

FindIt is a unified model for referring expression comprehension (col. 1), text-based localization (col. 2), and the object detection task (col. 3). FindIt can respond accurately when tested on object types/classes not known during training, e.g. “Find the desk” (col. 4). Compared to existing baselines (MattNet and GPV), FindIt can perform these tasks well and in a single model.

Multi-level Image-Text Fusion
Different localization tasks are created with different semantic understanding objectives. For example, because the referring expression task primarily references prominent objects in the image rather than small, occluded or faraway objects, low resolution images generally suffice. In contrast, the detection task aims to detect objects with various sizes and occlusion levels in higher resolution images. Apart from these benchmarks, the general visual grounding problem is inherently multiscale, as natural queries can refer to objects of any size. This motivates the need for a multi-level image-text fusion model for efficient processing of higher resolution images over different localization tasks.

The premise of FindIt is to fuse the higher level semantic features using more expressive transformer layers, which can capture all-pair interactions between image and text. For the lower-level and higher-resolution features, we use a cheaper dot-product fusion to save computation and memory cost. We attach a detector head (e.g., Faster R-CNN) on top of the fused feature maps to predict the boxes and their classes.

FindIt accepts an image and a query text as inputs, and processes them separately in image/text backbones before applying the multi-level fusion. We feed the fused features to Faster R-CNN to predict the boxes referred to by the text. The feature fusion uses more expressive transformers at higher levels and cheaper dot-product at the lower levels.

Multitask Learning
Apart from the multi-level fusion described above, we adapt the text-based localization and detection tasks to take the same inputs as the referring expression comprehension task. For the text-based localization task, we generate a set of queries over the categories present in the image. For any present category, the text query takes the form “Find the [object],” where [object] is the category name. The objects corresponding to that category are labeled as foreground and the other objects as background. Instead of using the aforementioned prompt, we use a static prompt for the detection task, such as “Find all the objects.”. We found that the specific choice of prompts is not important for text-based localization and detection tasks.

After adaptation, all tasks in consideration share the same inputs and outputs — an image input, a text query, and a set of output bounding boxes and classes. We then combine the datasets and train on the mixture. Finally, we use the standard object detection losses for all tasks, which we found to be surprisingly simple and effective.

Evaluation
We apply FindIt to the popular RefCOCO benchmark for referring expression comprehension tasks. When only the COCO and RefCOCO dataset is available, FindIt outperforms the state-of-the-art-model on all tasks. In the settings where external datasets are allowed, FindIt sets a new state of the art by using COCO and all RefCOCO splits together (no other datasets). On the challenging Google and UMD splits, FindIt outperforms the state of the art by a 10% margin, which, taken together, demonstrate the benefits of multitask learning.

Comparison with the state of the art on the popular referring expression benchmark. FindIt is superior on both the COCO and unconstrained settings (additional training data allowed).

On the text-based localization benchmark, FindIt achieves 79.7%, higher than the GPV (73.0%), and Faster R-CNN baselines (75.2%). Please refer to the paper for more quantitative evaluation.

We further observe that FindIt generalizes better to novel categories and super-categories in the text-based localization task compared to competitive single-task baselines on the popular COCO and Objects365 datasets, shown in the figure below.

FindIt on novel and super categories. Left: FindIt outperforms the single-task baselines especially on the novel categories. Right: FindIt outperforms the single-task baselines on the unseen super categories. “Rec-Single” is the Referring expression comprehension single task model and “Loc-Single” is the text-based localization single task model.

Efficiency
We also benchmark the inference times on the referring expression comprehension task (see Table below). FindIt is efficient and comparable with existing one-stage approaches while achieving higher accuracy. For fair comparison, all running times are measured on one GTX 1080Ti GPU.

Conclusion
We present Findit, which unifies referring expression comprehension, text-based localization, and object detection tasks. We propose multi-scale cross-attention to unify the diverse localization requirements of these tasks. Without any task-specific design, FindIt surpasses the state of the art on referring expression and text-based localization, shows competitive performance on detection, and generalizes better to out-of-distribution data and novel classes. All of these are accomplished in a single, unified, and efficient model.

Acknowledgements
This work is conducted by Weicheng Kuo, Fred Bertsch, Wei Li, AJ Piergiovanni, Mohammad Saffar, and Anelia Angelova. We would like to thank Ashish Vaswani, Prajit Ramachandran, Niki Parmar, David Luan, Tsung-Yi Lin, and other colleagues at Google Research for their advice and helpful discussions. We would like to thank Tom Small for preparing the animation.

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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.

¹Formalized in the equation below: ^↩

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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, 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.

–>

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.

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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.

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