b'Kaiming He'

research

∙ 05/18/2022

Masked Autoencoders As Spatiotemporal Learners

This paper studies a conceptually simple extension of Masked Autoencoder...

Christoph Feichtenhofer, et al. ∙

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

∙ 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

∙ 11/11/2021

Masked Autoencoders Are Scalable Vision Learners

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

Kaiming He, 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

∙ 04/05/2021

An Empirical Study of Training Self-Supervised Vision Transformers

This paper does not describe a novel method. Instead, it studies a strai...

Xinlei Chen, et al. ∙

research

∙ 11/20/2020

Exploring Simple Siamese Representation Learning

Siamese networks have become a common structure in various recent models...

Xinlei Chen, et al. ∙

research

∙ 07/13/2020

Graph Structure of Neural Networks

Neural networks are often represented as graphs of connections between n...

Jiaxuan You, 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

∙ 04/21/2019

Deep Hough Voting for 3D Object Detection in Point Clouds

Current 3D object detection methods are heavily influenced by 2D detecto...

Charles R. Qi, 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

∙ 12/10/2018

SlowFast Networks for Video Recognition

We present SlowFast networks for video recognition. Our model involves (...

Christoph Feichtenhofer, et al. ∙

research

∙ 12/09/2018

Feature Denoising for Improving Adversarial Robustness

Adversarial attacks to image classification systems present challenges t...

Cihang Xie, 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

∙ 06/14/2018

GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

Modern deep transfer learning approaches have mainly focused on learning...

Zhilin Yang, 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

∙ 03/22/2018

Group Normalization

Batch Normalization (BN) is a milestone technique in the development of ...

Yuxin Wu, 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

∙ 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

∙ 08/09/2017

Transitive Invariance for Self-supervised Visual Representation Learning

Learning visual representations with self-supervised learning has become...

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

∙ 03/20/2017

Mask R-CNN

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

Kaiming He, 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

∙ 05/20/2016

R-FCN: Object Detection via Region-based Fully Convolutional Networks

We present region-based, fully convolutional networks for accurate and e...

Jifeng Dai, et al. ∙

research

∙ 04/18/2016

ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation

Large-scale data is of crucial importance for learning semantic segmenta...

Di Lin, et al. ∙

research

∙ 03/29/2016

Instance-sensitive Fully Convolutional Networks

Fully convolutional networks (FCNs) have been proven very successful for...

Jifeng Dai, et al. ∙

research

∙ 03/16/2016

Identity Mappings in Deep Residual Networks

Deep residual networks have emerged as a family of extremely deep archit...

Kaiming He, et al. ∙

research

∙ 12/14/2015

Instance-aware Semantic Segmentation via Multi-task Network Cascades

Semantic segmentation research has recently witnessed rapid progress, bu...

Jifeng Dai, et al. ∙

research

∙ 12/10/2015

Deep Residual Learning for Image Recognition

Deeper neural networks are more difficult to train. We present a residua...

Kaiming He, 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

∙ 05/26/2015

Accelerating Very Deep Convolutional Networks for Classification and Detection

This paper aims to accelerate the test-time computation of convolutional...

Xiangyu Zhang, et al. ∙

research

∙ 05/05/2015

Fast Guided Filter

The guided filter is a technique for edge-aware image filtering. Because...

Kaiming He, et al. ∙

research

∙ 04/23/2015

Object Detection Networks on Convolutional Feature Maps

Most object detectors contain two important components: a feature extrac...

Shaoqing Ren, et al. ∙

research

∙ 03/05/2015

BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

Recent leading approaches to semantic segmentation rely on deep convolut...

Jifeng Dai, et al. ∙

research

∙ 02/06/2015

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

Rectified activation units (rectifiers) are essential for state-of-the-a...

Kaiming He, et al. ∙

research

∙ 12/31/2014

Image Super-Resolution Using Deep Convolutional Networks

We propose a deep learning method for single image super-resolution (SR)...

Chao Dong, et al. ∙

research

∙ 12/04/2014

Convolutional Neural Networks at Constrained Time Cost

Though recent advanced convolutional neural networks (CNNs) have been im...

Kaiming He, et al. ∙

research

∙ 12/03/2014

Convolutional Feature Masking for Joint Object and Stuff Segmentation

The topic of semantic segmentation has witnessed considerable progress d...

Jifeng Dai, et al. ∙

Kaiming He

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