Summary of Langxai: Integrating Large Vision Models For Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks, by Truong Thanh Hung Nguyen et al.
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception Tasks
by Truong Thanh Hung Nguyen, Tobias Clement, Phuc Truong Loc Nguyen, Nils Kemmerzell, Van Binh Truong, Vo Thanh Khang Nguyen, Mohamed Abdelaal, Hung Cao
First submitted to arxiv on: 19 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 This paper introduces LangXAI, a framework that combines Explainable Artificial Intelligence (XAI) with advanced vision models to generate textual explanations for visual recognition tasks. By integrating XAI with vision models, LangXAI aims to bridge the understanding gap between AI experts and non-experts by providing text-based explanations for classification, object detection, and semantic segmentation model outputs. The authors demonstrate the effectiveness of LangXAI through preliminary results showing high BERTScore across tasks, leading to a more transparent and reliable AI framework for vision tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LangXAI is a tool that helps computers understand what they are seeing by giving them words to describe what’s in pictures or videos. Right now, there’s a problem because not everyone knows how AI works, so LangXAI is trying to fix this by adding words to explain what AI models are doing when they make decisions. The people who made LangXAI tested it and found that it does a great job of explaining things in a way that makes sense. |
Keywords
» Artificial intelligence » Classification » Object detection » Semantic segmentation
