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About this paper
Xiaoliu Luo, Minxue Xiao, Ting Xie, Mengzhu Wang, Huiqing Qi, Joey Tianyi Zhou, Taiping Zhang, Xu Wang
A framework called Multi-View Synergistic Learning (MVSL) integrates modality-aware adaptation, multi-granularity representation alignment, and class-level semantic structure modeling to enhance the performance of Vision-Language Models in low-resource biomedical image classification. MVSL achieved 4.71% higher average accuracy in K=16 few-shot settings and a 77.22% harmonic mean in base-to-novel generalization across 11 biomedical datasets.
Key insights
- Decoupling adaptation strategies for visual and textual encoders based on their distinct data representations leads to more stable and effective VLM fine-tuning in specialized domains.
- Capturing both global image semantics and localized lesion-level evidence through multi-granularity alignment significantly enhances fine-grained discrimination in biomedical images.
- Integrating class-level semantic structure via a Disease Semantic Graph provides crucial regularization for textual embeddings, improving inter-class discriminability for visually similar conditions.
Summary by alphaXiv.
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