research-article

Discriminative Segment Focus Network for Fine-grained Video Action Recognition

Authors:

Zhiyong WangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 7

Article No.: 218, Pages 1 - 20

https://doi.org/10.1145/3654671

Published: 15 May 2024 Publication History

Abstract

Fine-grained video action recognition aims at identifying minor and discriminative variations among fine categories of actions. While many recent action recognition methods have been proposed to better model spatio-temporal representations, how to model the interactions among discriminative atomic actions to effectively characterize inter-class and intra-class variations has been neglected, which is vital for understanding fine-grained actions. In this work, we devise a Discriminative Segment Focus Network (DSFNet) to mine the discriminability of segment correlations and localize discriminative action-relevant segments for fine-grained video action recognition. Firstly, we propose a hierarchic correlation reasoning (HCR) module which explicitly establishes correlations between different segments at multiple temporal scales and enhances each segment by exploiting the correlations with other segments. Secondly, a discriminative segment focus (DSF) module is devised to localize the most action-relevant segments from the enhanced representations of HCR by enforcing the consistency between the discriminability and the classification confidence of a given segment with a consistency constraint. Finally, these localized segment representations are combined with the global action representation of the whole video for boosting final recognition. Extensive experimental results on two fine-grained action recognition datasets, i.e., FineGym and Diving48, and two action recognition datasets, i.e., Kinetics400 and Something-Something, demonstrate the effectiveness of our approach compared with the state-of-the-art methods.

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Cited By

Zhang JLi Y(2025)SL-GCNN: A Graph Convolutional Neural Network for Granular Human Motion RecognitionIEEE Access10.1109/ACCESS.2024.351408213(12373-12387)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2024.3514082
Zhou LZhang WZhang BLi XZhu J(2025)A strong benchmark for yoga action recognition based on lightweight pose estimation modelMultimedia Systems10.1007/s00530-024-01646-931:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s00530-024-01646-9

Index Terms

Discriminative Segment Focus Network for Fine-grained Video 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 7

July 2024

973 pages

EISSN:1551-6865

DOI:10.1145/3613662

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

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Publication History

Published: 15 May 2024

Online AM: 26 March 2024

Accepted: 20 March 2024

Revised: 24 February 2024

Received: 20 December 2023

Published in TOMM Volume 20, Issue 7

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Cited By

Zhang JLi Y(2025)SL-GCNN: A Graph Convolutional Neural Network for Granular Human Motion RecognitionIEEE Access10.1109/ACCESS.2024.351408213(12373-12387)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2024.3514082
Zhou LZhang WZhang BLi XZhu J(2025)A strong benchmark for yoga action recognition based on lightweight pose estimation modelMultimedia Systems10.1007/s00530-024-01646-931:1Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1007/s00530-024-01646-9

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