Gram-Anchored Prompt Learning for Vision-Language Models via Second-Order Statistics
Parameter-efficient prompt learning has become the de facto standard for adapting Vision-Language Models (VLMs) to downstream tasks. Existing approaches predominantly focus on aligning text prompts with first-order visual features (i.e., spatial feature maps). While effective for fine-grained semantic discrimination, we argue that relying solely on first-order information is insufficient for robust adaptation, as these spatially entangled features are highly susceptible to domain shifts and local noise. In this work, we propose \textbf{Gram-Anchored Prompt Learning (GAPL)} for Vision-Language Models via Second-Order Statistics, a framework that synergizes local semantic alignment with glo...
| Authors | Minglei Chen, Weilong Wang, Jiang Duan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 April 2026 |
| Last updated at source | 5 April 2026 |
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