AI RESEARCH
UbiQVision: Quantifying Uncertainty in XAI for Image Recognition
arXiv CS.CV
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ArXi:2512.20288v2 Announce Type: replace 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.