Research Area
Year Published

430 Results

October 28, 2019

Unsupervised Pre-Training of Image Features on Non-Curated Data

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.

By: Mathilde Caron, Piotr Bojanowski, Julien Mairal, Armand Joulin

October 27, 2019

Video Classification with Channel-Separated Convolutional Networks

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.

By: Du Tran, Heng Wang, Lorenzo Torresani, Matt Feiszli

October 27, 2019

Compositional Video Prediction

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.

By: Yufei Ye, Maneesh Singh, Abhinav Gupta, Shubham Tulsiani

October 27, 2019

Canonical Surface Mapping via Geometric Cycle Consistency

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.

By: Nilesh Kulkarni, Abhinav Gupta, Shubham Tulsiani

October 27, 2019

Scaling and Benchmarking Self-Supervised Visual Representation Learning

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.

By: Priya Goyal, Dhruv Mahajan, Abhinav Gupta, Ishan Misra

October 26, 2019

On Network Design Spaces for Visual Recognition

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.

By: Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, Piotr Dollar

August 15, 2019

PHYRE: A New Benchmark for Physical Reasoning

Understanding and reasoning about physics is an important ability of intelligent agents. We develop the PHYRE benchmark for physical reasoning that contains a set of simple classical mechanics puzzles in a 2D physical environment.

By: Anton Bakhtin, Laurens van der Maaten, Justin Johnson, Laura Gustafson, Ross Girshick

August 12, 2019

Efficient Segmentation: Learning Downsampling Near Semantic Boundaries

International Conference on Computer Vision (ICCV)

Many automated processes such as auto-piloting rely on a good semantic segmentation as a critical component. To speed up performance, it is common to downsample the input frame. However, this comes at the cost of missed small objects and reduced accuracy at semantic boundaries. To address this problem, we propose a new content-adaptive downsampling technique that learns to favor sampling locations near semantic boundaries of target classes.

By: Dmitrii Marin, Zijian He, Peter Vajda, Priyam Chatterjee, Sam Tsai, Fei Yang, Yuri Boykov

August 4, 2019

MSURU: Large Scale E-commerce Image Classification With Weakly Supervised Search Data

Conference on Knowledge Discovery and Data Mining (KDD)

In this paper we present a deployed image recognition system used in a large scale commerce search engine, which we call MSURU. It is designed to process product images uploaded daily to Facebook Marketplace. Social commerce is a growing area within Facebook and understanding visual representations of product content is important for search and recommendation applications on Marketplace.

By: Yina Tang, Fedor Borisyuk, Siddarth Malreddy, Yixuan Li, Yiqun Liu, Sergey Kirshner

August 1, 2019

Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks

Annual Meeting of the Association for Computational Linguistics (ACL)

Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks.

By: Yi Tay, Aston Zhang, Luu Anh Tuan, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui