DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models

• URL: http://arxiv.org/abs/2505.19373v1
• Date: Mon, 26 May 2025 00:14:52 GMT
• Title: DiSa: Directional Saliency-Aware Prompt Learning for Generalizable Vision-Language Models
• Authors: Niloufar Alipour Talemi, Hossein Kashiani, Hossein R. Nowdeh, Fatemeh Afghah,
• Abstract summary: DiSa is a Directional Saliency-Aware Prompt Learning framework.<n>It integrates two complementary regularization strategies to enhance generalization.<n>It consistently outperforms state-of-the-art prompt learning methods across various settings.
• Score: 5.027492394254859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Prompt learning has emerged as a powerful paradigm for adapting vision-language models such as CLIP to downstream tasks. However, existing methods often overfit to seen data, leading to significant performance degradation when generalizing to novel classes or unseen domains. To address this limitation, we propose DiSa, a Directional Saliency-Aware Prompt Learning framework that integrates two complementary regularization strategies to enhance generalization. First, our Cross-Interactive Regularization (CIR) fosters cross-modal alignment by enabling cooperative learning between prompted and frozen encoders. Within CIR, a saliency-aware masking strategy guides the image encoder to prioritize semantically critical image regions, reducing reliance on less informative patches. Second, we introduce a directional regularization strategy that aligns visual embeddings with class-wise prototype features in a directional manner to prioritize consistency in feature orientation over strict proximity. This approach ensures robust generalization by leveraging stable prototype directions derived from class-mean statistics. Extensive evaluations on 11 diverse image classification benchmarks demonstrate that DiSa consistently outperforms state-of-the-art prompt learning methods across various settings, including base-to-novel generalization, cross-dataset transfer, domain generalization, and few-shot learning.

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