Topology-Aware CLIP Few-Shot Learning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915272514863104 |
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| author | Huang, Dazhi |
| author_facet | Huang, Dazhi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_01694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Topology-Aware CLIP Few-Shot Learning Huang, Dazhi Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Topology-Aware CLIP Few-Shot Learning |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.01694 |