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April 20, 2026IEEE Transactions on Image Processing

Vision-Language Collaborative Representation Learning for Action Quality Assessment

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Authors

KGKumie GedamuYJYanli JiWZWangmeng Zuo

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Overview

This approach reveals improved action quality assessment in video sequences, indicating better accuracy in evaluations.

Key Points

  • The aim is to enhance action quality assessment by integrating vision and language features in a unified representation.
  • Developed a Vision-Language Collaboration Representation Learning framework (VLC-Net).
  • Implemented bidirectional knowledge distillation for collaboration learning between modalities.
  • Designed alignment guidance to unify action features across vision and language.
  • Utilized multimodal contrastive learning for aligning subactions with textual descriptions.
  • Demonstrated superior performance compared to state-of-the-art methods.
  • Achieved improved accuracy in predicting AQA scores.
  • Successfully aligned action semantics across video and text modalities.

Cite This Study

Gedamu et al. (2026) studied this question.

synapsesocial.com/papers/69e5c22d03c29399140288fchttps://doi.org/10.1109/tip.2026.3683256
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