research-article

Attentional Composition Networks for Long-Tailed Human Action Recognition

Authors: Haoran Wang, Yajie Wang, Baosheng Yu, Yibing Zhan, Chunfeng Yuan, Wankou YangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 1

Article No.: 8, Pages 1 - 18

https://doi.org/10.1145/3603253

Published: 24 August 2023 Publication History

Abstract

The problem of long-tailed visual recognition has been receiving increasing research attention. However, the long-tailed distribution problem remains underexplored for video-based visual recognition. To address this issue, in this article we propose a compositional learning based solution for video-based human action recognition. Our method, named Attentional Composition Networks (ACN), first learns verb-like and preposition-like components, then shuffles these components to generate samples for the tail classes in the feature space to augment the data for the tail classes. Specifically, during training, we represent each action video by a graph that captures the spatial-temporal relations (edges) among detected human/object instances (nodes). Then, ACN utilizes the position information to decompose each action into a set of verb and preposition representations using the edge features in the graph. After that, the verb and preposition features from different videos are combined via an attention structure to synthesize feature representations for tail classes. This way, we can enrich the data for the tail classes and consequently improve the action recognition for these classes. To evaluate the compositional human action recognition, we further contribute a new human action recognition dataset, namely NEU-Interaction (NEU-I). Experimental results on both Something-Something V2 and the proposed NEU-I demonstrate the effectiveness of the proposed method for long-tailed, few-shot, and zero-shot problems in human action recognition. Source code and the NEU-I dataset are available at https://github.com/YajieW99/ACN.

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Index Terms

Attentional Composition Networks for Long-Tailed Human Action Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 1

January 2024

639 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3613542

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Publication History

Published: 24 August 2023

Online AM: 09 June 2023

Accepted: 22 May 2023

Revised: 21 March 2023

Received: 23 October 2022

Published in TOMM Volume 20, Issue 1

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Major Science and Technology Innovation 2030 “New Generation Artificial Intelligence” key project
Fundamental Research Funds for the Central Universities of China
National Nature Science Foundation of China

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Gao HSu YWang FLi H(2024)Heterogeneous Fusion and Integrity Learning Network for RGB-D Salient Object DetectionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/365647620:7(1-24)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3656476
Wang SYan YHan FTian YDing YYang PLi XOkoshi TKo JLiKamWa R(2024)MultiRider: Enabling Multi-Tag Concurrent OFDM Backscatter by Taming In-band InterferenceProceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services10.1145/3643832.3661862(292-303)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3643832.3661862
Xue MXu ZQiao SZheng JLi TWang YPeng D(2024)Driver intention prediction based on multi-dimensional cross-modality information interactionMultimedia Systems10.1007/s00530-024-01282-330:2Online publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1007/s00530-024-01282-3

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