Constrained Prompt Enhancement for Improving Zero-Shot Generalization of Vision-Language Models

TL;DR

This paper introduces a novel constrained prompt enhancement method that constructs comprehensive textual prompts and compact visual prompts to improve zero-shot generalization of vision-language models by better aligning visual and textual information.

Contribution

The paper proposes TGSSG and CADRS techniques to generate semantic-rich textual prompts and noise-reduced visual prompts, enhancing visual-textual alignment in VLMs.

Findings

Improved zero-shot performance on benchmark datasets.

Effective filtering of visual noise with CADRS.

Enhanced semantic coverage in textual prompts with TGSSG.

Abstract

Vision-language models (VLMs) pre-trained on web-scale data exhibit promising zero-shot generalization but often suffer from semantic misalignment due to domain gaps between pre-training and downstream tasks. Existing approaches primarily focus on text prompting with class-specific descriptions and visual-text adaptation via aligning cropped image regions with textual descriptions. However, they still face the issues of incomplete textual prompts and noisy visual prompts. In this paper, we propose a novel constrained prompt enhancement (CPE) method to improve visual-textual alignment by constructing comprehensive textual prompts and compact visual prompts from the semantic perspective. Specifically, our approach consists of two key components: Topology-Guided Synonymous Semantic Generation (TGSSG) and Category-Agnostic Discriminative Region Selection (CADRS). Textually, to address the issue of incomplete semantic expression in textual prompts, our TGSSG first generates synonymous semantic set for each category via large language models, and constructs comprehensive textual prompts based on semantic ambiguity entropy and persistent homology analysis. Visually, to mitigate the irrelevant visual noise introduced by random cropping, our CADRS identifies discriminative regions with activation maps outputted by a pre-trained vision model, effectively filtering out noisy regions and generating compact visual prompts. Given the comprehensive set of textual prompts and compact set of visual prompts, we introduce two set-to-set matching strategies based on test-time adaptation (TTA) and optimal transport (OT) to achieve effective visual-textual alignment, and so improve zero-shot generalization of VLMs.