Low-Rank Prompt Learning for Vision-Language Models with Fixed-Token Bases
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arXiv:2609.09462v1 Announce Type: new Abstract: Prompt learning adapts CLIP to downstream recognition by replacing hand-written templates with learned continuous context vectors, which in Context Optimization (CoOp) form a dense prompt matrix $\mathbf{P}\in\mathbb{R}^{m\times d}$ trained from only…
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- 2026-09-10 04:00 · arXiv cs.CV
Low-Rank Prompt Learning for Vision-Language Models with Fixed-Token Bases