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StepNet: Spatial-temporal Part-aware Network for Isolated Sign Language Recognition

Authors:

Yi YangAuthors Info & Claims

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

Article No.: 226, Pages 1 - 19

https://doi.org/10.1145/3656046

Published: 16 May 2024 Publication History

Abstract

The goal of sign language recognition (SLR) is to help those who are hard of hearing or deaf overcome the communication barrier. Most existing approaches can be typically divided into two lines, i.e., Skeleton-based, and RGB-based methods, but both lines of methods have their limitations. Skeleton-based methods do not consider facial expressions, while RGB-based approaches usually ignore the fine-grained hand structure. To overcome both limitations, we propose a new framework called the Spatial-temporal Part-aware network (StepNet), based on RGB parts. As its name suggests, it is made up of two modules: Part-level Spatial Modeling and Part-level Temporal Modeling. Part-level Spatial Modeling, in particular, automatically captures the appearance-based properties, such as hands and faces, in the feature space without the use of any keypoint-level annotations. On the other hand, Part-level Temporal Modeling implicitly mines the long short-term context to capture the relevant attributes over time. Extensive experiments demonstrate that our StepNet, thanks to spatial-temporal modules, achieves competitive Top-1 Per-instance accuracy on three commonly used SLR benchmarks, i.e., 56.89% on WLASL, 77.2% on NMFs-CSL, and 77.1% on BOBSL. Additionally, the proposed method is compatible with the optical flow input and can produce superior performance if fused. For those who are hard of hearing, we hope that our work can act as a preliminary step.

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Cited By

Lin XLiu MChen H(2024)Spike-HAR++: an energy-efficient and lightweight parallel spiking transformer for event-based human action recognitionFrontiers in Computational Neuroscience10.3389/fncom.2024.150829718Online publication date: 26-Nov-2024
https://doi.org/10.3389/fncom.2024.1508297
Alves CDe A. Boldt FPaixão T(2024)Enhancing Brazilian Sign Language Recognition Through Skeleton Image Representation2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI62404.2024.10716301(1-6)Online publication date: 30-Sep-2024
https://doi.org/10.1109/SIBGRAPI62404.2024.10716301
Deng ZLeng YHu JLin ZLi XGao Q(2024)SML: A Skeleton-based multi-feature learning method for sign language recognitionKnowledge-Based Systems10.1016/j.knosys.2024.112288301(112288)Online publication date: Oct-2024
https://doi.org/10.1016/j.knosys.2024.112288

Index Terms

StepNet: Spatial-temporal Part-aware Network for Isolated Sign Language Recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Activity recognition and understanding
        Video summarization

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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: 16 May 2024

Online AM: 03 April 2024

Accepted: 14 March 2024

Revised: 13 February 2024

Received: 08 October 2023

Published in TOMM Volume 20, Issue 7

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Lin XLiu MChen H(2024)Spike-HAR++: an energy-efficient and lightweight parallel spiking transformer for event-based human action recognitionFrontiers in Computational Neuroscience10.3389/fncom.2024.150829718Online publication date: 26-Nov-2024
https://doi.org/10.3389/fncom.2024.1508297
Alves CDe A. Boldt FPaixão T(2024)Enhancing Brazilian Sign Language Recognition Through Skeleton Image Representation2024 37th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)10.1109/SIBGRAPI62404.2024.10716301(1-6)Online publication date: 30-Sep-2024
https://doi.org/10.1109/SIBGRAPI62404.2024.10716301
Deng ZLeng YHu JLin ZLi XGao Q(2024)SML: A Skeleton-based multi-feature learning method for sign language recognitionKnowledge-Based Systems10.1016/j.knosys.2024.112288301(112288)Online publication date: Oct-2024
https://doi.org/10.1016/j.knosys.2024.112288

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