Summary of Hmgie: Hierarchical and Multi-grained Inconsistency Evaluation For Vision-language Data Cleansing, by Zihao Zhu et al.
HMGIE: Hierarchical and Multi-Grained Inconsistency Evaluation for Vision-Language Data Cleansing
by Zihao Zhu, Hongbao Zhang, Guanzong Wu, Siwei Lyu, Baoyuan Wu
First submitted to arxiv on: 7 Dec 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 The paper presents a novel framework for evaluating visual-textual inconsistency (VTI) in image captioning datasets. The main challenges in VTI evaluation arise from the diversity of datasets, which can introduce inconsistencies at different levels of granularity. To address this, the authors propose Hierarchical and Multi-Grained Inconsistency Evaluation (HMGIE), a three-module framework that provides multi-grained evaluations for accuracy and completeness. HMGIE generates a semantic graph from the image caption, performs hierarchical inconsistency evaluation using dynamic question-answer generation and evaluation, and calculates quantitative scores with natural language explanations. The authors also introduce MVTID, an image-caption dataset with diverse inconsistencies, and demonstrate the superiority of HMGIE over current state-of-the-art methods through extensive experiments on multiple benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to evaluate how well images match their written descriptions. Right now, it’s hard to compare different ways of describing the same image because there are so many differences in what people write and what they mean by certain words. The authors created a special tool that looks at these differences and gives a score for how well the image matches its description. This tool is called HMGIE, and it has three parts: making a map of what’s in the image, checking if everything on the map makes sense, and giving a final score based on how well everything matches up. The authors also made their own special dataset with lots of different types of image descriptions to test this tool. They found that HMGIE works much better than other tools at evaluating these descriptions. |
Keywords
» Artificial intelligence » Image captioning
