GaVA-CLIP:Refining Multimodal Representations with Clinical Knowledge and Numerical Parameters for Gait Video Analysis in Neurodegenerative Diseases.

Abstract

We present GaVA-CLIP, a knowledge augmentation strategy for Gait Video Analysis, designed to assess diagnostic groups and gait impairment. Based on the large-scale pretrained Vision Language Model, CLIP, GaVA-CLIP learns and enhances visual, textual, and numerical representations of patient gait videos through collective learning across three distinct modalities: gait videos, class-specific descriptions, and numerical gait parameters. Our specific contributions are two-fold: First, we adopt a knowledge-aware prompt tuning strategy to utilize class-specific medical descriptions in guiding text prompt learning. Second, we integrate paired gait parameters as numerical texts to enhance the numeracy of textual representations. Results demonstrate that GaVA-CLIP not only significantly outperforms state-of-the-art (SOTA) methods in video-based classification tasks but also adeptly decodes the learned class-specific text features into natural language descriptions using the vocabulary of quantitative gait parameters. The code and the model will be made available at our project page: https://lisqzqng.github.io/GaitAnalysisVLM/.