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Found 4 papers, 2 papers with code
CLIP-QDA: An Explainable Concept Bottleneck Model
In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification.
Learning to Generate Training Datasets for Robust Semantic Segmentation
We propose a novel approach to improve the robustness of semantic segmentation techniques by leveraging the synergy between label-to-image generators and image-to-label segmentation models.
MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks
However, disentangling the different types and sources of uncertainty is non trivial for most datasets, especially since there is no ground truth for uncertainty.
A study of deep perceptual metrics for image quality assessment
Several metrics exist to quantify the similarity between images, but they are inefficient when it comes to measure the similarity of highly distorted images.
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