Vision Language Model Based Caption Evaluation Method Leveraging Visual Context Extraction
Keywords: Image Captioning, Evaluation, Vision and Language, LLM as a judge
Abstract: Given the accelerating progress of vision and language modeling, accurate evaluation of machine-generated image captions remains critical. In order to evaluate captions more closely to human preferences, metrics need to discriminate between captions of varying quality and content. However, conventional metrics fall short of comparing beyond superficial matches of words or embedding similarities; thus, they still need improvement. This paper presents VisCE2, a vision language model-based caption evaluation method. Our method focuses on visual context, which refers to the detailed content of images, including objects, attributes, and relationships. By extracting and organizing them into a structured format, we replace the human-written references with visual contexts and help VLMs better understand the image, enhancing evaluation performance. Through meta-evaluation on multiple datasets, we validated that VisCE2 outperforms the conventional pre-trained metrics in capturing caption quality and demonstrates superior consistency with human judgment.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 4186
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