AttriPrompt: Dynamic Prompt Composition Learning for CLIP

• URL: http://arxiv.org/abs/2509.05949v1
• Date: Sun, 07 Sep 2025 07:07:59 GMT
• Title: AttriPrompt: Dynamic Prompt Composition Learning for CLIP
• Authors: Qiqi Zhan, Shiwei Li, Qingjie Liu, Yunhong Wang,
• Abstract summary: AttriPrompt is a novel framework that enhances and refines textual semantic representations.<n>We introduce a Self-Regularization mechanism by applying explicit regularization constraints between the prompted and non-prompted text features.<n>Experiments demonstrate AttriPrompt's superiority over state-of-the-art methods, achieving up to 7.37% improvement in the base-to-novel setting.
• Score: 41.37140060183439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evolution of prompt learning methodologies has driven exploration of deeper prompt designs to enhance model performance. However, current deep text prompting approaches suffer from two critical limitations: Over-reliance on constrastive learning objectives that prioritize high-level semantic alignment, neglecting fine-grained feature optimization; Static prompts across all input categories, preventing content-aware adaptation. To address these limitations, we propose AttriPrompt-a novel framework that enhances and refines textual semantic representations by leveraging the intermediate-layer features of CLIP's vision encoder. We designed an Attribute Retrieval module that first clusters visual features from each layer. The aggregated visual features retrieve semantically similar prompts from a prompt pool, which are then concatenated to the input of every layer in the text encoder. Leveraging hierarchical visual information embedded in prompted text features, we introduce Dual-stream Contrastive Learning to realize fine-grained alignment. Furthermore, we introduce a Self-Regularization mechanism by applying explicit regularization constraints between the prompted and non-prompted text features to prevent overfitting on limited training data. Extensive experiments across three benchmarks demonstrate AttriPrompt's superiority over state-of-the-art methods, achieving up to 7.37\% improvement in the base-to-novel setting. The observed strength of our method in cross-domain knowledge transfer positions vision-language pre-trained models as more viable solutions for real-world implementation.

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