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End-to-end Temporal Action Detection with Transformer

2021-06-18 17:58:34
Xiaolong Liu, Qimeng Wang, Yao Hu, Xu Tang, Song Bai, Xiang Bai

Abstract

Temporal action detection (TAD) aims to determine the semantic label and the boundaries of every action instance in an untrimmed video. It is a fundamental task in video understanding and significant progress has been made in TAD. Previous methods involve multiple stages or networks and hand-designed rules or operations, which fall short in efficiency and flexibility. Here, we construct an end-to-end framework for TAD upon Transformer, termed \textit{TadTR}, which simultaneously predicts all action instances as a set of labels and temporal locations in parallel. TadTR is able to adaptively extract temporal context information needed for making action predictions, by selectively attending to a number of snippets in a video. It greatly simplifies the pipeline of TAD and runs much faster than previous detectors. Our method achieves state-of-the-art performance on HACS Segments and THUMOS14 and competitive performance on ActivityNet-1.3. Our code will be made available at \url{this https URL}.

Abstract (translated)

URL

https://arxiv.org/abs/2106.10271

PDF

https://arxiv.org/pdf/2106.10271.pdf


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