Enhancing Zero-Shot Image Recognition in Vision-Language Models through Human-like Concept Guidance

• URL: http://arxiv.org/abs/2503.15886v2
• Date: Fri, 21 Mar 2025 02:55:26 GMT
• Title: Enhancing Zero-Shot Image Recognition in Vision-Language Models through Human-like Concept Guidance
• Authors: Hui Liu, Wenya Wang, Kecheng Chen, Jie Liu, Yibing Liu, Tiexin Qin, Peisong He, Xinghao Jiang, Haoliang Li,
• Abstract summary: In zero-shot image recognition tasks, humans demonstrate remarkable flexibility in classifying unseen categories.<n>Existing vision-language models often underperform in real-world applications because of sub-optimal prompt engineering.<n>We propose a Concept-guided Human-like Bayesian Reasoning framework to address these issues.
• Score: 41.6755826072905
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In zero-shot image recognition tasks, humans demonstrate remarkable flexibility in classifying unseen categories by composing known simpler concepts. However, existing vision-language models (VLMs), despite achieving significant progress through large-scale natural language supervision, often underperform in real-world applications because of sub-optimal prompt engineering and the inability to adapt effectively to target classes. To address these issues, we propose a Concept-guided Human-like Bayesian Reasoning (CHBR) framework. Grounded in Bayes' theorem, CHBR models the concept used in human image recognition as latent variables and formulates this task by summing across potential concepts, weighted by a prior distribution and a likelihood function. To tackle the intractable computation over an infinite concept space, we introduce an importance sampling algorithm that iteratively prompts large language models (LLMs) to generate discriminative concepts, emphasizing inter-class differences. We further propose three heuristic approaches involving Average Likelihood, Confidence Likelihood, and Test Time Augmentation (TTA) Likelihood, which dynamically refine the combination of concepts based on the test image. Extensive evaluations across fifteen datasets demonstrate that CHBR consistently outperforms existing state-of-the-art zero-shot generalization methods.

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