Summary of Heie: Mllm-based Hierarchical Explainable Aigc Image Implausibility Evaluator, by Fan Yang et al.
HEIE: MLLM-Based Hierarchical Explainable AIGC Image Implausibility Evaluator
by Fan Yang, Ru Zhen, Jianing Wang, Yanhao Zhang, Haoxiang Chen, Haonan Lu, Sicheng Zhao, Guiguang Ding
First submitted to arxiv on: 26 Nov 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 proposes HEIE, a novel multimodal large language model (MLLM) based hierarchical explainable image implausibility evaluator for predicting defect region heatmaps in augmented and generated images. The proposed approach addresses two primary challenges: lack of explainability and inability to leverage common sense and logical reasoning. The method integrates heatmaps, scores, and explanation outputs using the CoT-Driven Explainable Trinity Evaluator, which decomposes complex tasks into subtasks of increasing difficulty. Additionally, it introduces an Adaptive Hierarchical Implausibility Mapper that enables precise local-to-global hierarchical heatmap predictions through an uncertainty-based adaptive token approach. The proposed method demonstrates state-of-the-art performance in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to solve a problem with fake images by creating a new tool that can explain why some parts of the image are weird. This tool uses big language models and special algorithms to find tiny mistakes in the images and make it easier for humans to understand what’s wrong. The authors also created a new dataset to test this tool and found that it works really well. |
Keywords
» Artificial intelligence » Large language model » Token
