Publications

November 10, 2019

Symposium on Foundations of Computer Science (FOCS)

We consider Bandits with Knapsacks (henceforth, BwK), a general model for multi-armed bandits under supply/budget constraints. In particular, a bandit algorithm needs to solve a well-known knapsack problem: find an optimal packing of items into a limited-size knapsack. The BwK problem is a common generalization of numerous motivating examples, which range from dynamic pricing to repeated auctions to dynamic ad allocation to network routing and scheduling.

Areas: Economics & Computation Facebook AI Research Machine Learning

October 28, 2019

International Conference on Computer Vision (ICCV)

Pre-training general-purpose visual features with convolutional neural networks without relying on annotations is a challenging and important task. Most recent efforts in unsupervised feature learning have focused on either small or highly curated datasets like ImageNet, whereas using non-curated raw datasets was found to decrease the feature quality when evaluated on a transfer task. Our goal is to bridge the performance gap between unsupervised methods trained on curated data, which are costly to obtain, and massive raw datasets that are easily available.

Areas: Computer Vision Facebook AI Research Machine Learning

October 27, 2019

International Conference on Computer Vision (ICCV)

Self-supervised learning aims to learn representations from the data itself without explicit manual supervision. Existing efforts ignore a crucial aspect of self-supervised learning – the ability to scale to large amount of data because self-supervision requires no manual labels. In this work, we revisit this principle and scale two popular self-supervised approaches to 100 million images.

Areas: Computer Vision Facebook AI Research

October 27, 2019

International Conference on Computer Vision (ICCV)

This paper studies the effects of different design choices in 3D group convolutional networks for video classification. We empirically demonstrate that the amount of channel interactions plays an important role in the accuracy of 3D group convolutional networks.

Areas: Computer Vision Facebook AI Research Machine Learning

October 27, 2019

International Conference on Computer Vision (ICCV)

We present an approach for pixel-level future prediction given an input image of a scene. We observe that a scene is comprised of distinct entities that undergo motion and present an approach that operationalizes this insight. We implicitly predict future states of independent entities while reasoning about their interactions, and compose future video frames using these predicted states.

Areas: Computer Vision Facebook AI Research

October 27, 2019

International Conference on Computer Vision (ICCV)

Video recognition models have progressed significantly over the past few years, evolving from shallow classifiers trained on hand-crafted features to deep spatiotemporal networks. However, labeled video data required to train such models has not been able to keep up with the ever increasing depth and sophistication of these networks. In this work we propose an alternative approach to learning video representations that requires no semantically labeled videos, and instead leverages the years of effort in collecting and labeling large and clean still-image datasets.

Areas: Computer Vision Facebook AI Research Machine Learning

October 27, 2019

International Conference on Computer Vision (ICCV)

Predicting future frames for a video sequence is a challenging generative modeling task. Promising approaches include probabilistic latent variable models such as the Variational Auto-Encoder. While VAEs can handle uncertainty and model multiple possible future outcomes, they have a tendency to produce blurry predictions. In this work we argue that this is a sign of underfitting.

Areas: Computer Vision Facebook AI Research Machine Learning

October 27, 2019

International Conference on Computer Vision (ICCV)

In this paper we introduce a lightweight “clip-sampling” model that can efficiently identify the most salient temporal clips within a long video. We demonstrate that the computational cost of action recognition on untrimmed videos can be dramatically reduced by invoking recognition only on these most salient clips. Furthermore, we show that this yields significant gains in recognition accuracy compared to analysis of all clips or randomly/uniformly selected clips.

Areas: Computer Vision Facebook AI Research Machine Learning

October 27, 2019

International Conference on Computer Vision (ICCV)

We explore the task of Canonical Surface Mapping (CSM). Specifically, given an image, we learn to map pixels on the object to their corresponding locations on an abstract 3D model of the category.

Areas: Computer Vision Facebook AI Research

October 26, 2019

International Conference on Computer Vision (ICCV)

Over the past several years progress in designing better neural network architectures for visual recognition has been substantial. To help sustain this rate of progress, in this work we propose to reexamine the methodology for comparing network architectures. In particular, we introduce a new comparison paradigm of distribution estimates, in which network design spaces are compared by applying statistical techniques to populations of sampled models, while controlling for confounding factors like network complexity.

Areas: Computer Vision Facebook AI Research