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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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11/11/2021
Masked Autoencoders Are Scalable Vision Learners
This paper shows that masked autoencoders (MAE) are scalable self-superv...
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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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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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12/10/2018
SlowFast Networks for Video Recognition
We present SlowFast networks for video recognition. Our model involves (...
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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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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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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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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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12/10/2015
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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05/05/2015
Fast Guided Filter
The guided filter is a technique for edge-aware image filtering. Because...
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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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06/18/2014
