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  3. Vision-language Collaborative Representation Learning For Action Quality Assessment.
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  3. Vision-language Collaborative Representation Learning For Action Quality Assessment.

Related Experiment Video

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control
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Vision-Language Collaborative Representation Learning for Action Quality Assessment.

Kumie Gedamu, Yanli Ji, Wangmeng Zuo

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 17, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    This study introduces Vision-Language Collaboration Representation Learning (VLC-Net) for improved Action Quality Assessment (AQA). VLC-Net enhances fine-grained action understanding and prediction accuracy by unifying vision and language features.

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    Area of Science:

    • Computer Vision and Machine Learning
    • Multimodal AI
    • Action Recognition and Understanding

    Background:

    • Action Quality Assessment (AQA) is crucial for real-world applications requiring detailed action sequence comprehension.
    • Existing multimodal approaches for AQA often suffer from instability and suboptimal performance due to directional bias in vision-language embedding spaces.
    • Reliance solely on textual information from language models limits the effectiveness of current AQA methods.

    Purpose of the Study:

    • To propose a novel Vision-Language Collaboration Representation Learning approach (VLC-Net) for accurate AQA score prediction.
    • To develop a unified feature representation that captures temporal dependencies in fine-grained action sequences.
    • To overcome the limitations of existing methods by addressing directional bias and improving multimodal feature integration.

    Main Methods:

    • Implemented a bidirectional knowledge distillation operation for collaborative learning between pre-trained vision-language models and visual action knowledge.
    • Designed vision-language alignment guidance to explicitly align action features with shared semantic meanings across modalities.
    • Utilized multimodal contrastive learning on aligned features to enhance the relationship between modalities and subactions with textual descriptions.

    Main Results:

    • VLC-Net demonstrated superior performance in fine-grained action sequence understanding and AQA score prediction.
    • The proposed methods effectively unified joint representations by aligning features across vision and language modalities.
    • Experimental results on multiple datasets (FineDiving, MTL-AQA, FineFS, Fis-V) show significant improvements over state-of-the-art methods.

    Conclusions:

    • VLC-Net effectively addresses the challenges of directional bias in vision-language embedding spaces for AQA.
    • The approach successfully learns unified representations of fine-grained actions by integrating visual and textual information.
    • The proposed method offers a robust and effective solution for accurate Action Quality Assessment, outperforming existing techniques.