Summary of Megl: Multimodal Explanation-guided Learning, by Yifei Zhang et al.
MEGL: Multimodal Explanation-Guided Learning
by Yifei Zhang, Tianxu Jiang, Bo Pan, Jingyu Wang, Guangji Bai, Liang Zhao
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 proposed Multimodal Explanation-Guided Learning (MEGL) framework aims to address the limitations of traditional eXplainable AI (XAI) methods by leveraging both visual and textual explanations for image classification tasks. The MEGL framework includes a novel Saliency-Driven Textual Grounding (SDTG) approach, which integrates spatial information from visual explanations into textual rationales, providing contextually rich explanations. Additionally, the framework introduces Textual Supervision on Visual Explanations to align visual explanations with textual rationales and a Visual Explanation Distribution Consistency loss to reinforce visual coherence. The MEGL framework outperforms previous approaches in prediction accuracy and explanation quality across both visual and textual domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way for artificial intelligence models to explain their decisions, making them more understandable. They combined two types of explanations: visual and textual. Visual explanations show important parts of an image, while textual explanations provide context about what’s happening in the image. The new method, called Multimodal Explanation-Guided Learning (MEGL), helps improve how well models work by providing a better understanding of their decisions. This can lead to more accurate predictions and better communication between humans and AI systems. |
Keywords
» Artificial intelligence » Grounding » Image classification
