MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching
为解决动作质量评估中忽视肌肉力学等问题，MyoMechanix通过多模态数据和结构化表示方法提供细粒度的生物力学反馈。
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Attention-Driven Multimodal Alignment for Long-term Action Quality Assessment
Existing long-form action quality assessment (AQA) methods suffer from two key limitations: unimodal approaches neglect critical auditory cues, while multimodal methods typically employ shallow feature fusion, lacking deep cross-modal collaboration and temporal dynamic modeling. To address insufficient audio-visual synergy in artistic sports videos, this paper proposes an attention-driven multimodal alignment framework. It introduces a local query encoder for fine-grained temporal alignment, a multimodal attention consistency mechanism to enhance cross-modal interaction, and a two-level scoring scheme to improve interpretability. The model is jointly optimized via attention loss and regression loss. Extensive experiments on the RG and Fis-V datasets demonstrate significant improvements over state-of-the-art methods, validating the framework’s effectiveness, robustness, and interpretability for long-sequence AQA.
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MyoMechanix: Biomechanically-Grounded Compositional Skilled Activity Understanding and Coaching
为解决动作质量评估中忽视肌肉力学等问题，MyoMechanix通过多模态数据和结构化表示方法提供细粒度的生物力学反馈。
0 citationsRead paper
Attention-Driven Multimodal Alignment for Long-term Action Quality Assessment
Existing long-form action quality assessment (AQA) methods suffer from two key limitations: unimodal approaches neglect critical auditory cues, while multimodal methods typically employ shallow feature fusion, lacking deep cross-modal collaboration and temporal dynamic modeling. To address insufficient audio-visual synergy in artistic sports videos, this paper proposes an attention-driven multimodal alignment framework. It introduces a local query encoder for fine-grained temporal alignment, a multimodal attention consistency mechanism to enhance cross-modal interaction, and a two-level scoring scheme to improve interpretability. The model is jointly optimized via attention loss and regression loss. Extensive experiments on the RG and Fis-V datasets demonstrate significant improvements over state-of-the-art methods, validating the framework’s effectiveness, robustness, and interpretability for long-sequence AQA.
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