Topology-Aware CLIP Few-Shot Learning

• URL: http://arxiv.org/abs/2505.01694v1
• Date: Sat, 03 May 2025 04:58:29 GMT
• Title: Topology-Aware CLIP Few-Shot Learning
• Authors: Dazhi Huang,
• Abstract summary: We introduce a topology-aware tuning approach integrating Representation Topology Divergence into the Task Residual framework.<n>By explicitly aligning the topological structures of visual and text representations using a combined RTD and Cross-Entropy loss, our method enhances few-shot performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Efficiently adapting large Vision-Language Models (VLMs) like CLIP for few-shot learning poses challenges in balancing pre-trained knowledge retention and task-specific adaptation. Existing methods often overlook valuable structural information within the VLM's latent space. We introduce a topology-aware tuning approach integrating Representation Topology Divergence (RTD) into the Task Residual (TR) framework. By explicitly aligning the topological structures of visual and text representations using a combined RTD and Cross-Entropy loss, while freezing base VLM encoders, our method enhances few-shot performance. We optimize only lightweight Task Residual parameters, effectively leveraging topological information. Across 6 diverse benchmark datasets, our approach demonstrates significant gains, achieving an average accuracy improvement of 1-2\% over relevant baseline methods in few-shot settings. This work presents an effective strategy to boost VLM few-shot capabilities by incorporating topological alignment.

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