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20152023
most citedIs Space-Time Attention All You Need for Video Understanding?

1.5k citations · 2k across the 15 of their papers we have counts for

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Showing 2021 · cs.CVShow all

5 papers · 2 filters

cs.CV2021★ 1 cited

PyTorchVideo: A Deep Learning Library for Video Understanding

Haoqi Fan, Tullie Murrell, Heng Wang +13

We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tas…

cs.CV2021

Searching for Two-Stream Models in Multivariate Space for Video Recognition

Xinyu Gong, Heng Wang, Zheng Shou +3

Conventional video models rely on a single stream to capture the complex spatial-temporal features. Recent work on two-stream video models, such as SlowFast network and AssembleNet…

cs.CV2021

Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation

Weiyao Wang, Matt Feiszli, Heng Wang +1

Current state-of-the-art object detection and segmentation methods work well under the closed-world assumption. This closed-world setting assumes that the list of object categories…

cs.CV2021

Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories

Xitong Yang, Haoqi Fan, Lorenzo Torresani +2

The standard way of training video models entails sampling at each iteration a single clip from a video and optimizing the clip prediction with respect to the video-level label. We…

cs.CV2021★ 1.5k cited

Is Space-Time Attention All You Need for Video Understanding?

Gedas Bertasius, Heng Wang, Lorenzo Torresani

We present a convolution-free approach to video classification built exclusively on self-attention over space and time. Our method, named "TimeSformer," adapts the standard Transfo…