Summary of Xedgeai: a Human-centered Industrial Inspection Framework with Data-centric Explainable Edge Ai Approach, by Truong Thanh Hung Nguyen et al.
XEdgeAI: A Human-centered Industrial Inspection Framework with Data-centric Explainable Edge AI Approach
by Truong Thanh Hung Nguyen, Phuc Truong Loc Nguyen, Hung Cao
First submitted to arxiv on: 16 Jul 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 paper introduces a novel XAI-integrated Visual Quality Inspection framework that optimizes the deployment of semantic segmentation models on low-resource edge devices. The framework incorporates XAI and the Large Vision Language Model to deliver human-centered interpretability through visual and textual explanations to end-users. It consists of six fundamental modules: base model fine-tuning, XAI-based explanation generation, evaluation of XAI approaches, XAI-guided data augmentation, development of an edge-compatible model, and the generation of understandable visual and textual explanations. The proposed framework is designed to address challenges in deploying deep learning technologies on low-resource edge devices, enabling real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem by making AI more reliable and interpretable in industrial settings. It’s like having a doctor explain what they found when you go for a check-up. The researchers created a special kind of AI that can work on small devices like smartphones. This helps people trust the AI results because they understand how it got those answers. The goal is to make AI better and more useful in industries where decisions need to be made quickly. |
Keywords
* Artificial intelligence * Data augmentation * Deep learning * Fine tuning * Language model * Semantic segmentation
