Summary of Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction, by Koki Maeda et al.
Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction
by Koki Maeda, Shuhei Kurita, Taiki Miyanishi, Naoaki Okazaki
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for evaluating machine-generated image captions is proposed, which utilizes a vision language model-based method called VisCE^2. The method focuses on visual context, extracting and organizing objects, attributes, and relationships within images to enhance evaluation performance. This paper presents a framework that replaces human-written references with visual contexts, allowing vision language models (VLMs) to better understand image content. The proposed method outperforms conventional pre-trained metrics in capturing caption quality and demonstrates superior consistency with human judgment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VisCE^2 is a new way to evaluate how well computer-generated captions match the images they describe. Right now, we don’t have a good way to measure how close these captions are to what humans would write. This paper shows that by looking at the details in an image, like objects and relationships, we can create a better way to judge caption quality. The result is a system that works well and agrees with human opinions. |
Keywords
» Artificial intelligence » Language model
