b'Ross Girshick'

research

∙ 03/30/2022

Exploring Plain Vision Transformer Backbones for Object Detection

We explore the plain, non-hierarchical Vision Transformer (ViT) as a bac...

Yanghao Li, et al. ∙

research

∙ 01/20/2022

Revisiting Weakly Supervised Pre-Training of Visual Perception Models

Model pre-training is a cornerstone of modern visual recognition systems...

Mannat Singh, et al. ∙

research

∙ 11/22/2021

Benchmarking Detection Transfer Learning with Vision Transformers

Object detection is a central downstream task used to test if pre-traine...

Yanghao Li, et al. ∙

research

∙ 11/18/2021

PyTorchVideo: A Deep Learning Library for Video Understanding

We introduce PyTorchVideo, an open-source deep-learning library that pro...

Haoqi Fan, et al. ∙

research

∙ 11/11/2021

Masked Autoencoders Are Scalable Vision Learners

This paper shows that masked autoencoders (MAE) are scalable self-superv...

Kaiming He, et al. ∙

research

∙ 06/28/2021

Early Convolutions Help Transformers See Better

Vision transformer (ViT) models exhibit substandard optimizability. In p...

Tete Xiao, et al. ∙

research

∙ 04/29/2021

A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning

We present a large-scale study on unsupervised spatiotemporal representa...

Christoph Feichtenhofer, et al. ∙

research

∙ 03/30/2021

Boundary IoU: Improving Object-Centric Image Segmentation Evaluation

We present Boundary IoU (Intersection-over-Union), a new segmentation ev...

Bowen Cheng, et al. ∙

research

∙ 03/11/2021

Fast and Accurate Model Scaling

In this work we analyze strategies for convolutional neural network scal...

Piotr Dollár, et al. ∙

research

∙ 02/01/2021

Evaluating Large-Vocabulary Object Detectors: The Devil is in the Details

By design, average precision (AP) for object detection aims to treat all...

Achal Dave, et al. ∙

research

∙ 05/16/2020

Large scale weakly and semi-supervised learning for low-resource video ASR

Many semi- and weakly-supervised approaches have been investigated for o...

Kritika Singh, et al. ∙

research

∙ 03/30/2020

Designing Network Design Spaces

In this work, we present a new network design paradigm. Our goal is to h...

Ilija Radosavovic, et al. ∙

research

∙ 03/26/2020

Are Labels Necessary for Neural Architecture Search?

Existing neural network architectures in computer vision — whether desig...

Chenxi Liu, et al. ∙

research

∙ 03/09/2020

Improved Baselines with Momentum Contrastive Learning

Contrastive unsupervised learning has recently shown encouraging progres...

Xinlei Chen, et al. ∙

research

∙ 12/17/2019

PointRend: Image Segmentation as Rendering

We present a new method for efficient high-quality image segmentation of...

Alexander Kirillov, et al. ∙

research

∙ 12/02/2019

A Multigrid Method for Efficiently Training Video Models

Training competitive deep video models is an order of magnitude slower t...

Chao-Yuan Wu, et al. ∙

research

∙ 11/13/2019

Momentum Contrast for Unsupervised Visual Representation Learning

We present Momentum Contrast (MoCo) for unsupervised visual representati...

Kaiming He, et al. ∙

research

∙ 10/27/2019

Training ASR models by Generation of Contextual Information

Supervised ASR models have reached unprecedented levels of accuracy, tha...

Kritika Singh, et al. ∙

research

∙ 08/15/2019

PHYRE: A New Benchmark for Physical Reasoning

Understanding and reasoning about physics is an important ability of int...

Anton Bakhtin, et al. ∙

research

∙ 08/08/2019

LVIS: A Dataset for Large Vocabulary Instance Segmentation

Progress on object detection is enabled by datasets that focus the resea...

Agrim Gupta, et al. ∙

research

∙ 04/02/2019

Exploring Randomly Wired Neural Networks for Image Recognition

Neural networks for image recognition have evolved through extensive man...

Saining Xie, et al. ∙

research

∙ 03/28/2019

TensorMask: A Foundation for Dense Object Segmentation

Sliding-window object detectors that generate bounding-box object predic...

Xinlei Chen, et al. ∙

research

∙ 01/08/2019

Panoptic Feature Pyramid Networks

The recently introduced panoptic segmentation task has renewed our commu...

Alexander Kirillov, et al. ∙

research

∙ 12/12/2018

Long-Term Feature Banks for Detailed Video Understanding

To understand the world, we humans constantly need to relate the present...

Chao-Yuan Wu, et al. ∙

research

∙ 11/21/2018

Rethinking ImageNet Pre-training

We report competitive results on object detection and instance segmentat...

Kaiming He, et al. ∙

research

∙ 05/02/2018

Exploring the Limits of Weakly Supervised Pretraining

State-of-the-art visual perception models for a wide range of tasks rely...

Dhruv Mahajan, et al. ∙

research

∙ 01/16/2018

Low-Shot Learning from Imaginary Data

Humans can quickly learn new visual concepts, perhaps because they can e...

Yu-Xiong Wang, et al. ∙

research

∙ 01/03/2018

Panoptic Segmentation

We propose and study a novel 'Panoptic Segmentation' (PS) task. Panoptic...

Alexander Kirillov, et al. ∙

research

∙ 12/12/2017

Data Distillation: Towards Omni-Supervised Learning

We investigate omni-supervised learning, a special regime of semi-superv...

Ilija Radosavovic, et al. ∙

research

∙ 12/04/2017

Learning by Asking Questions

We introduce an interactive learning framework for the development and t...

Ishan Misra, et al. ∙

research

∙ 11/28/2017

Learning to Segment Every Thing

Existing methods for object instance segmentation require all training i...

Ronghang Hu, et al. ∙

research

∙ 11/21/2017

Non-local Neural Networks

Both convolutional and recurrent operations are building blocks that pro...

Xiaolong Wang, et al. ∙

research

∙ 08/07/2017

Focal Loss for Dense Object Detection

The highest accuracy object detectors to date are based on a two-stage a...

Tsung-Yi Lin, et al. ∙

research

∙ 06/08/2017

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Deep learning thrives with large neural networks and large datasets. How...

Priya Goyal, et al. ∙

research

∙ 05/10/2017

Inferring and Executing Programs for Visual Reasoning

Existing methods for visual reasoning attempt to directly map inputs to ...

Justin Johnson, et al. ∙

research

∙ 03/20/2017

Mask R-CNN

We present a conceptually simple, flexible, and general framework for ob...

Kaiming He, et al. ∙

research

∙ 12/20/2016

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

When building artificial intelligence systems that can reason and answer...

Justin Johnson, et al. ∙

research

∙ 12/19/2016

Learning Features by Watching Objects Move

This paper presents a novel yet intuitive approach to unsupervised featu...

Deepak Pathak, et al. ∙

research

∙ 12/09/2016

Feature Pyramid Networks for Object Detection

Feature pyramids are a basic component in recognition systems for detect...

Tsung-Yi Lin, et al. ∙

research

∙ 11/16/2016

Aggregated Residual Transformations for Deep Neural Networks

We present a simple, highly modularized network architecture for image c...

Saining Xie, et al. ∙

research

∙ 06/09/2016

Low-shot Visual Recognition by Shrinking and Hallucinating Features

Low-shot visual learning---the ability to recognize novel object categor...

Bharath Hariharan, et al. ∙

research

∙ 04/13/2016

Visual Storytelling

We introduce the first dataset for sequential vision-to-language, and ex...

Ting-Hao, et al. ∙

research

∙ 04/13/2016

Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks

As 3D movie viewing becomes mainstream and Virtual Reality (VR) market e...

Junyuan Xie, et al. ∙

research

∙ 04/12/2016

Training Region-based Object Detectors with Online Hard Example Mining

The field of object detection has made significant advances riding on th...

Abhinav Shrivastava, et al. ∙

research

∙ 12/22/2015

Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels

When human annotators are given a choice about what to label in an image...

Ishan Misra, et al. ∙

research

∙ 12/14/2015

Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

It is well known that contextual and multi-scale representations are imp...

Sean Bell, et al. ∙

research

∙ 11/19/2015

Reducing Overfitting in Deep Networks by Decorrelating Representations

One major challenge in training Deep Neural Networks is preventing overf...

Michael Cogswell, et al. ∙

research

∙ 06/08/2015

You Only Look Once: Unified, Real-Time Object Detection

We present YOLO, a new approach to object detection. Prior work on objec...

Joseph Redmon, et al. ∙

research

∙ 06/04/2015

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

State-of-the-art object detection networks depend on region proposal alg...

Shaoqing Ren, et al. ∙

research

∙ 05/05/2015

Contextual Action Recognition with R*CNN

There are multiple cues in an image which reveal what action a person is...

Georgia Gkioxari, et al. ∙

Ross Girshick

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