Summary of Vale: a Multimodal Visual and Language Explanation Framework For Image Classifiers Using Explainable Ai and Language Models, by Purushothaman Natarajan and Athira Nambiar
VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language Models
by Purushothaman Natarajan, Athira Nambiar
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 novel multimodal framework, VALE Visual and Language Explanation, combines explainable AI techniques with advanced language models to provide comprehensive explanations. The framework integrates visual explanations from XAI tools, an advanced zero-shot image segmentation model, and a visual language model to generate corresponding textual explanations. This approach bridges the semantic gap between machine outputs and human interpretation, delivering results that are more comprehensible to users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VALE is a new way to understand how Deep Neural Networks (DNNs) work. Right now, DNNs are like black boxes – we don’t know why they make certain decisions. This makes it hard to use them in important situations where mistakes could have big consequences. VALE is trying to change that by making it easier for humans to understand what DNNs do. It does this by combining different types of explanations, like pictures and words, to help people understand how the network works. |
Keywords
» Artificial intelligence » Image segmentation » Language model » Zero shot
