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Explainable Action Form Assessment by Exploiting Multimodal Chain-of-Thoughts Reasoning
Summary
This study introduces a new task and dataset for assessing human action form, providing detailed feedback for improvement. The developed Explainable Fitness Assessor framework enhances action analysis and feedback generation.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Current video understanding methods fail to assess action standardization and provide actionable feedback.
- Existing datasets lack labels for action standardization degree and explainable feedback for quality assessment.
Purpose of the Study:
- Define a new Human Action Form Assessment (AFA) task.
- Introduce the CoT-AFA dataset with multi-level annotations for fitness and martial arts videos.
- Develop an Explainable Fitness Assessor (EFA) framework for action judgment, explanation, and solution provision.
Main Methods:
- Introduced a novel Chain-of-Thought explanation paradigm for comprehensive reasoning.
- Proposed the Explainable Fitness Assessor (EFA) framework with parallel processing streams and a dynamic gating mechanism.
- Fused visual and semantic information for enhanced analytical capabilities.
Main Results:
- Achieved significant improvements in explanation generation (+16.0% in CIDEr).
- Enhanced action classification accuracy by +2.7%.
- Improved action quality assessment accuracy by +2.1%.
Conclusions:
- The CoT-AFA dataset and EFA framework show great potential for advancing human action analysis and feedback.
- The proposed method offers a comprehensive approach to action assessment, explanation, and improvement.
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