SAMPLe: SAM-based Optimizer for Prompt Learning in VLMs
Pre-trained Vision-Language Models (VLMs) like CLIP have proven highly effective as foundation models for various downstream applications. However, prompt learning in VLMs encounters a performance-generalization dilemma: while prompts can be tuned to achieve high accuracy on seen distributions, this tuning process often undermines their generalizability to unseen data. The limited set of learnable prompts, which contextualize and condition the input to steer it toward the task within the pretrained VLM, tends to overfit the training data, leading to a trade-off between task-specific performance and preserving generalization. To address this dilemma, we introduce SAMPLe (Sharpness-Aware Mi...
| Authors | Hossein Rajoli, Fatemeh Lotfi, Niloufar Alipour Talemi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
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