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Related Experiment Videos

Confidence-Aware Gated Multimodal Fusion for Robust Temporal Action Localization in Occluded Environments.

Masato Takami1, Tomohiro Fukuda1

  • 1Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, The University of Osaka, 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan.

Sensors (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a Gated Skeleton Refinement Module (Gated SRM) to improve temporal action localization (TAL) in industrial settings. The Gated SRM enhances multimodal fusion robustness by using confidence scores to refine skeletal data, even with occlusions.

Keywords:
multimodal learningocclusion handlingpose estimationrobustnesstemporal action localization

Related Experiment Videos

Area of Science:

  • Computer Vision
  • Machine Learning
  • Robotics

Background:

  • Robust Temporal Action Localization (TAL) is critical for industrial monitoring.
  • Skeletal data in industrial settings suffers from occlusions, hindering multimodal fusion.
  • Existing methods often fail due to unreliable skeletal data transfer.

Purpose of the Study:

  • To develop a novel Gated Skeleton Refinement Module (Gated SRM) for robust TAL.
  • To address the negative transfer issue caused by occluded skeletal data.
  • To improve the reliability of multimodal fusion in challenging industrial environments.

Main Methods:

  • Proposed a Gated Skeleton Refinement Module (Gated SRM) as a front-end preprocessing step.
  • Incorporated OpenPose confidence scores as a logarithmic bias in a self-attention mechanism for soft suppression of unreliable joints.
  • Utilized a learnable gating network for adaptive fusion of refined skeletal features with RGB data.

Main Results:

  • Achieved a mean Average Precision (mAP) of 21.77% on the IKEA ASM dataset, demonstrating superior robustness under occlusion.
  • Effectively prevented accuracy degradation common in naive and established multimodal fusion strategies.
  • Maintained an end-to-end inference speed of 9.2 FPS, suitable for industrial workflow monitoring.

Conclusions:

  • The Gated SRM offers a robust and principled sensor-metadata-driven approach for action recognition under occlusion.
  • Prioritizing confidence-based data selection over data restoration provides a critical fail-safe for real-world applications.
  • The method enhances multimodal fusion reliability without compromising practical inference speeds.