MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning

• URL: http://arxiv.org/abs/2501.01834v3
• Date: Mon, 27 Jan 2025 16:34:59 GMT
• Title: MoColl: Agent-Based Specific and General Model Collaboration for Image Captioning
• Authors: Pu Yang, Bin Dong,
• Abstract summary: This paper proposes a novel agent-enhanced model collaboration framework called MoColl. MoColl decomposes complex image captioning tasks into a series of interconnected question-answer subtasks. Experimental results on radiology report generation validate the effectiveness of the proposed framework.
• Score: 4.955697042432618
• License:
• Abstract: Image captioning is a critical task at the intersection of computer vision and natural language processing, with wide-ranging applications across various domains. For complex tasks such as diagnostic report generation, deep learning models require not only domain-specific image-caption datasets but also the incorporation of relevant general knowledge to provide contextual accuracy. Existing approaches exhibit inherent limitations: specialized models excel in capturing domain-specific details but lack generalization, while vision-language models (VLMs) built on large language models (LLMs) leverage general knowledge but struggle with domain-specific adaptation. To address these limitations, this paper proposes a novel agent-enhanced model collaboration framework, which we call MoColl, designed to effectively integrate domain-specific and general knowledge. Specifically, our approach is to decompose complex image captioning tasks into a series of interconnected question-answer subtasks. A trainable visual question answering (VQA) model is employed as a specialized tool to focus on domain-specific visual analysis, answering task-specific questions based on image content. Concurrently, an LLM-based agent with general knowledge formulates these questions and synthesizes the resulting question-answer pairs into coherent captions. Beyond its role in leveraging the VQA model, the agent further guides its training to enhance its domain-specific capabilities. Experimental results on radiology report generation validate the effectiveness of the proposed framework, demonstrating significant improvements in the quality of generated reports.

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