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12/01/2022
Scaling Language-Image Pre-training via Masking
We present Fast Language-Image Pre-training (FLIP), a simple and more ef...
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05/18/2022
Masked Autoencoders As Spatiotemporal Learners
This paper studies a conceptually simple extension of Masked Autoencoder...
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03/30/2022
Exploring Plain Vision Transformer Backbones for Object Detection
We explore the plain, non-hierarchical Vision Transformer (ViT) as a bac...
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11/22/2021
Benchmarking Detection Transfer Learning with Vision Transformers
Object detection is a central downstream task used to test if pre-traine...
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11/11/2021
Masked Autoencoders Are Scalable Vision Learners
This paper shows that masked autoencoders (MAE) are scalable self-superv...
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04/29/2021
A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning
We present a large-scale study on unsupervised spatiotemporal representa...
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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...
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11/20/2020
Exploring Simple Siamese Representation Learning
Siamese networks have become a common structure in various recent models...
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07/13/2020
Graph Structure of Neural Networks
Neural networks are often represented as graphs of connections between n...
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03/30/2020
Designing Network Design Spaces
In this work, we present a new network design paradigm. Our goal is to h...
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03/26/2020
Are Labels Necessary for Neural Architecture Search?
Existing neural network architectures in computer vision — whether desig...
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03/09/2020
Improved Baselines with Momentum Contrastive Learning
Contrastive unsupervised learning has recently shown encouraging progres...
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12/17/2019
PointRend: Image Segmentation as Rendering
We present a new method for efficient high-quality image segmentation of...
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12/02/2019
A Multigrid Method for Efficiently Training Video Models
Training competitive deep video models is an order of magnitude slower t...
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11/13/2019
Momentum Contrast for Unsupervised Visual Representation Learning
We present Momentum Contrast (MoCo) for unsupervised visual representati...
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04/21/2019
Deep Hough Voting for 3D Object Detection in Point Clouds
Current 3D object detection methods are heavily influenced by 2D detecto...
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04/02/2019
Exploring Randomly Wired Neural Networks for Image Recognition
Neural networks for image recognition have evolved through extensive man...
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03/28/2019
TensorMask: A Foundation for Dense Object Segmentation
Sliding-window object detectors that generate bounding-box object predic...
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01/08/2019
Panoptic Feature Pyramid Networks
The recently introduced panoptic segmentation task has renewed our commu...
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12/12/2018
Long-Term Feature Banks for Detailed Video Understanding
To understand the world, we humans constantly need to relate the present...
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12/10/2018
SlowFast Networks for Video Recognition
We present SlowFast networks for video recognition. Our model involves (...
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12/09/2018
Feature Denoising for Improving Adversarial Robustness
Adversarial attacks to image classification systems present challenges t...
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11/21/2018
Rethinking ImageNet Pre-training
We report competitive results on object detection and instance segmentat...
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06/14/2018
GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations
Modern deep transfer learning approaches have mainly focused on learning...
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05/02/2018
Exploring the Limits of Weakly Supervised Pretraining
State-of-the-art visual perception models for a wide range of tasks rely...
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03/22/2018
Group Normalization
Batch Normalization (BN) is a milestone technique in the development of ...
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01/03/2018
Panoptic Segmentation
We propose and study a novel 'Panoptic Segmentation' (PS) task. Panoptic...
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12/12/2017
Data Distillation: Towards Omni-Supervised Learning
We investigate omni-supervised learning, a special regime of semi-superv...
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11/28/2017
Learning to Segment Every Thing
Existing methods for object instance segmentation require all training i...
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11/21/2017
Non-local Neural Networks
Both convolutional and recurrent operations are building blocks that pro...
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08/09/2017
Transitive Invariance for Self-supervised Visual Representation Learning
Learning visual representations with self-supervised learning has become...
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08/07/2017
Focal Loss for Dense Object Detection
The highest accuracy object detectors to date are based on a two-stage a...
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06/08/2017
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
Deep learning thrives with large neural networks and large datasets. How...
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03/20/2017
Mask R-CNN
We present a conceptually simple, flexible, and general framework for ob...
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12/09/2016
Feature Pyramid Networks for Object Detection
Feature pyramids are a basic component in recognition systems for detect...
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11/16/2016
Aggregated Residual Transformations for Deep Neural Networks
We present a simple, highly modularized network architecture for image c...
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05/20/2016
R-FCN: Object Detection via Region-based Fully Convolutional Networks
We present region-based, fully convolutional networks for accurate and e...
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04/18/2016
ScribbleSup: Scribble-Supervised Convolutional Networks for Semantic Segmentation
Large-scale data is of crucial importance for learning semantic segmenta...
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03/29/2016
Instance-sensitive Fully Convolutional Networks
Fully convolutional networks (FCNs) have been proven very successful for...
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03/16/2016
Identity Mappings in Deep Residual Networks
Deep residual networks have emerged as a family of extremely deep archit...
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12/14/2015
Instance-aware Semantic Segmentation via Multi-task Network Cascades
Semantic segmentation research has recently witnessed rapid progress, bu...
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12/10/2015
Deep Residual Learning for Image Recognition
Deeper neural networks are more difficult to train. We present a residua...
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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...
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05/26/2015
Accelerating Very Deep Convolutional Networks for Classification and Detection
This paper aims to accelerate the test-time computation of convolutional...
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05/05/2015
Fast Guided Filter
The guided filter is a technique for edge-aware image filtering. Because...
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04/23/2015
Object Detection Networks on Convolutional Feature Maps
Most object detectors contain two important components: a feature extrac...
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03/05/2015
BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation
Recent leading approaches to semantic segmentation rely on deep convolut...
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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...
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12/31/2014
Image Super-Resolution Using Deep Convolutional Networks
We propose a deep learning method for single image super-resolution (SR)...
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12/04/2014
