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20162026
most citedMasked Autoencoders As Spatiotemporal Learners

244 citations · 772 across the 26 of their papers we have counts for

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7 papers · 1 filter

cs.CV2022★ 18 cited

MAViL: Masked Audio-Video Learners

Po-Yao Huang, Vasu Sharma, Hu Xu +7

We present Masked Audio-Video Learners (MAViL) to train audio-visual representations. Our approach learns with three complementary forms of self-supervision: (1) reconstruction of…

cs.CV2022★ 5 cited

Scaling Language-Image Pre-training via Masking

Yanghao Li, Haoqi Fan, Ronghang Hu +2

We present Fast Language-Image Pre-training (FLIP), a simple and more efficient method for training CLIP. Our method randomly masks out and removes a large portion of image patches…

cs.CV2022

Where is my Wallet? Modeling Object Proposal Sets for Egocentric Visual Query Localization

Mengmeng Xu, Yanghao Li, Cheng-Yang Fu +3

This paper deals with the problem of localizing objects in image and video datasets from visual exemplars. In particular, we focus on the challenging problem of egocentric visual q…

cs.CV2022★ 4 cited

Negative Frames Matter in Egocentric Visual Query 2D Localization

Mengmeng Xu, Cheng-Yang Fu, Yanghao Li +3

The recently released Ego4D dataset and benchmark significantly scales and diversifies the first-person visual perception data. In Ego4D, the Visual Queries 2D Localization task ai…

cs.CV2022★ 244 cited

Masked Autoencoders As Spatiotemporal Learners

Christoph Feichtenhofer, Haoqi Fan, Yanghao Li +1

This paper studies a conceptually simple extension of Masked Autoencoders (MAE) to spatiotemporal representation learning from videos. We randomly mask out spacetime patches in vid…

cs.CV2022★ 44 cited

Exploring Plain Vision Transformer Backbones for Object Detection

Yanghao Li, Hanzi Mao, Ross Girshick +1

We explore the plain, non-hierarchical Vision Transformer (ViT) as a backbone network for object detection. This design enables the original ViT architecture to be fine-tuned for o…

cs.CV2022★ 9 cited

MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video Recognition

Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam +4

While today's video recognition systems parse snapshots or short clips accurately, they cannot connect the dots and reason across a longer range of time yet. Most existing video ar…