Ross Girshick

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Facebook ∙ Microsoft ∙ berkeley college

Research Scientist at Facebook AI Research, Post-doctoral fellow at UC Berkeley

Exploring Randomly Wired Neural Networks for Image Recognition

Neural networks for image recognition have evolved through extensive man...
04/02/2019 ∙ by Saining Xie, et al. ∙ 36 ∙ share

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TensorMask: A Foundation for Dense Object Segmentation

Sliding-window object detectors that generate bounding-box object predic...
03/28/2019 ∙ by Xinlei Chen, et al. ∙ 28 ∙ share

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PointRend: Image Segmentation as Rendering

We present a new method for efficient high-quality image segmentation of...
12/17/2019 ∙ by Alexander Kirillov, et al. ∙ 28 ∙ share

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Momentum Contrast for Unsupervised Visual Representation Learning

We present Momentum Contrast (MoCo) for unsupervised visual representati...
11/13/2019 ∙ by Kaiming He, et al. ∙ 25 ∙ share

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Panoptic Feature Pyramid Networks

The recently introduced panoptic segmentation task has renewed our commu...
01/08/2019 ∙ by Alexander Kirillov, et al. ∙ 10 ∙ share

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Rethinking ImageNet Pre-training

We report competitive results on object detection and instance segmentat...
11/21/2018 ∙ by Kaiming He, et al. ∙ 8 ∙ share

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A Multigrid Method for Efficiently Training Video Models

Training competitive deep video models is an order of magnitude slower t...
12/02/2019 ∙ by Chao-Yuan Wu, et al. ∙ 7 ∙ share

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Long-Term Feature Banks for Detailed Video Understanding

To understand the world, we humans constantly need to relate the present...
12/12/2018 ∙ by Chao-Yuan Wu, et al. ∙ 4 ∙ share

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PHYRE: A New Benchmark for Physical Reasoning

Understanding and reasoning about physics is an important ability of int...
08/15/2019 ∙ by Anton Bakhtin, et al. ∙ 4 ∙ share

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LVIS: A Dataset for Large Vocabulary Instance Segmentation

Progress on object detection is enabled by datasets that focus the resea...
08/08/2019 ∙ by Agrim Gupta, et al. ∙ 2 ∙ share

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Learning to Segment Every Thing

Existing methods for object instance segmentation require all training i...
11/28/2017 ∙ by Ronghang Hu, et al. ∙ 0 ∙ share

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Non-local Neural Networks

Both convolutional and recurrent operations are building blocks that pro...
11/21/2017 ∙ by Xiaolong Wang, et al. ∙ 0 ∙ share

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Focal Loss for Dense Object Detection

The highest accuracy object detectors to date are based on a two-stage a...
08/07/2017 ∙ by Tsung-Yi Lin, et al. ∙ 0 ∙ share

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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Deep learning thrives with large neural networks and large datasets. How...
06/08/2017 ∙ by Priya Goyal, et al. ∙ 0 ∙ share

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Mask R-CNN

We present a conceptually simple, flexible, and general framework for ob...
03/20/2017 ∙ by Kaiming He, et al. ∙ 0 ∙ share

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Learning Features by Watching Objects Move

This paper presents a novel yet intuitive approach to unsupervised featu...
12/19/2016 ∙ by Deepak Pathak, et al. ∙ 0 ∙ share

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Feature Pyramid Networks for Object Detection

Feature pyramids are a basic component in recognition systems for detect...
12/09/2016 ∙ by Tsung-Yi Lin, et al. ∙ 0 ∙ share

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Aggregated Residual Transformations for Deep Neural Networks

We present a simple, highly modularized network architecture for image c...
11/16/2016 ∙ by Saining Xie, et al. ∙ 0 ∙ share

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Reducing Overfitting in Deep Networks by Decorrelating Representations

One major challenge in training Deep Neural Networks is preventing overf...
11/19/2015 ∙ by Michael Cogswell, et al. ∙ 0 ∙ share

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Low-shot Visual Recognition by Shrinking and Hallucinating Features

Low-shot visual learning---the ability to recognize novel object categor...
06/09/2016 ∙ by Bharath Hariharan, et al. ∙ 0 ∙ share

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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...
04/13/2016 ∙ by Junyuan Xie, et al. ∙ 0 ∙ share

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Training Region-based Object Detectors with Online Hard Example Mining

The field of object detection has made significant advances riding on th...
04/12/2016 ∙ by Abhinav Shrivastava, et al. ∙ 0 ∙ share

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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...
12/22/2015 ∙ by Ishan Misra, et al. ∙ 0 ∙ share

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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...
12/14/2015 ∙ by Sean Bell, et al. ∙ 0 ∙ share

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

We present YOLO, a new approach to object detection. Prior work on objec...
06/08/2015 ∙ by Joseph Redmon, et al. ∙ 0 ∙ share

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

State-of-the-art object detection networks depend on region proposal alg...
06/04/2015 ∙ by Shaoqing Ren, et al. ∙ 0 ∙ share

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Contextual Action Recognition with R*CNN

There are multiple cues in an image which reveal what action a person is...
05/05/2015 ∙ by Georgia Gkioxari, et al. ∙ 0 ∙ share

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Fast R-CNN

This paper proposes a Fast Region-based Convolutional Network method (Fa...
04/30/2015 ∙ by Ross Girshick, et al. ∙ 0 ∙ share

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Object Detection Networks on Convolutional Feature Maps

Most object detectors contain two important components: a feature extrac...
04/23/2015 ∙ by Shaoqing Ren, et al. ∙ 0 ∙ share

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Inferring 3D Object Pose in RGB-D Images

The goal of this work is to replace objects in an RGB-D scene with corre...
02/16/2015 ∙ by Saurabh Gupta, et al. ∙ 0 ∙ share

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Actions and Attributes from Wholes and Parts

We investigate the importance of parts for the tasks of action and attri...
12/08/2014 ∙ by Georgia Gkioxari, et al. ∙ 0 ∙ share

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Hypercolumns for Object Segmentation and Fine-grained Localization

Recognition algorithms based on convolutional networks (CNNs) typically ...
11/21/2014 ∙ by Bharath Hariharan, et al. ∙ 0 ∙ share

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Deformable Part Models are Convolutional Neural Networks

Deformable part models (DPMs) and convolutional neural networks (CNNs) a...
09/18/2014 ∙ by Ross Girshick, et al. ∙ 0 ∙ share

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Learning Rich Features from RGB-D Images for Object Detection and Segmentation

In this paper we study the problem of object detection for RGB-D images ...
07/22/2014 ∙ by Saurabh Gupta, et al. ∙ 0 ∙ share

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LSDA: Large Scale Detection Through Adaptation

A major challenge in scaling object detection is the difficulty of obtai...
07/18/2014 ∙ by Judy Hoffman, et al. ∙ 0 ∙ share

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Part-based R-CNNs for Fine-grained Category Detection

Semantic part localization can facilitate fine-grained categorization by...
07/15/2014 ∙ by Ning Zhang, et al. ∙ 0 ∙ share

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Simultaneous Detection and Segmentation

We aim to detect all instances of a category in an image and, for each i...
07/07/2014 ∙ by Bharath Hariharan, et al. ∙ 0 ∙ share

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Analyzing the Performance of Multilayer Neural Networks for Object Recognition

In the last two years, convolutional neural networks (CNNs) have achieve...
07/07/2014 ∙ by Pulkit Agrawal, et al. ∙ 0 ∙ share

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Caffe: Convolutional Architecture for Fast Feature Embedding

Caffe provides multimedia scientists and practitioners with a clean and ...
06/20/2014 ∙ by Yangqing Jia, et al. ∙ 0 ∙ share

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R-CNNs for Pose Estimation and Action Detection

We present convolutional neural networks for the tasks of keypoint (pose...
06/19/2014 ∙ by Georgia Gkioxari, et al. ∙ 0 ∙ share

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Microsoft COCO: Common Objects in Context

We present a new dataset with the goal of advancing the state-of-the-art...
05/01/2014 ∙ by Tsung-Yi Lin, et al. ∙ 0 ∙ share

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Rich feature hierarchies for accurate object detection and semantic segmentation

Object detection performance, as measured on the canonical PASCAL VOC da...
11/11/2013 ∙ by Ross Girshick, et al. ∙ 0 ∙ share

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Inferring and Executing Programs for Visual Reasoning

Existing methods for visual reasoning attempt to directly map inputs to ...
05/10/2017 ∙ by Justin Johnson, et al. ∙ 0 ∙ share

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

When building artificial intelligence systems that can reason and answer...
12/20/2016 ∙ by Justin Johnson, et al. ∙ 0 ∙ share

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Visual Storytelling

We introduce the first dataset for sequential vision-to-language, and ex...
04/13/2016 ∙ by Ting-Hao, et al. ∙ 0 ∙ share

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Data Distillation: Towards Omni-Supervised Learning

We investigate omni-supervised learning, a special regime of semi-superv...
12/12/2017 ∙ by Ilija Radosavovic, et al. ∙ 0 ∙ share

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Panoptic Segmentation

We propose and study a novel 'Panoptic Segmentation' (PS) task. Panoptic...
01/03/2018 ∙ by Alexander Kirillov, et al. ∙ 0 ∙ share

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Learning by Asking Questions

We introduce an interactive learning framework for the development and t...
12/04/2017 ∙ by Ishan Misra, et al. ∙ 0 ∙ share

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Low-Shot Learning from Imaginary Data

Humans can quickly learn new visual concepts, perhaps because they can e...
01/16/2018 ∙ by Yu-Xiong Wang, et al. ∙ 0 ∙ share

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Exploring the Limits of Weakly Supervised Pretraining

State-of-the-art visual perception models for a wide range of tasks rely...
05/02/2018 ∙ by Dhruv Mahajan, et al. ∙ 0 ∙ share

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Ross Girshick

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