UbiQVision: Quantifying Uncertainty in XAI for Image Recognition

📰 ArXiv cs.AI

Learn to quantify uncertainty in XAI for image recognition using UbiQVision, enhancing model explainability and interpretability

advanced Published 25 Apr 2026

Action Steps

Apply SHAP to image recognition models to generate explanations
Configure UbiQVision to quantify uncertainty in XAI
Test the performance of UbiQVision on benchmark datasets
Compare the results with state-of-the-art XAI methods
Run UbiQVision on real-world image recognition tasks to evaluate its effectiveness

Who Needs to Know This

Data scientists and machine learning engineers working on image recognition tasks can benefit from this research to improve model transparency and trustworthiness

Key Insight

💡 UbiQVision provides a framework to quantify uncertainty in XAI, improving model trustworthiness and interpretability

Key Takeaways

Learn to quantify uncertainty in XAI for image recognition using UbiQVision, enhancing model explainability and interpretability

Full Article

Title: UbiQVision: Quantifying Uncertainty in XAI for Image Recognition

Abstract:
arXiv:2512.20288v2 Announce Type: replace-cross Abstract: Recent advances in deep learning have led to its widespread adoption across diverse domains, including medical imaging. This progress is driven by increasingly sophisticated model architectures, such as ResNets, Vision Transformers, and Hybrid Convolutional Neural Networks, that offer enhanced performance at the cost of greater complexity. This complexity often compromises model explainability and interpretability. SHAP has emerged as a p

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