A gradient-attention explainability method produces sequence-level visual and textual saliency maps for free-form answers from large vision-language models, with stronger human-attention alignment and faithfulness than prior baselines.
Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling
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abstract
Deep neural network models for image segmentation can be a powerful tool for the automation of motor claims handling processes in the insurance industry. A crucial aspect is the reliability of the model outputs when facing adverse conditions, such as low quality photos taken by claimants to document damages. We explore the use of a meta-classification model to empirically assess the precision of segments predicted by a model trained for the semantic segmentation of car body parts. Different sets of features correlated with the quality of a segment are compared, and an AUROC score of 0.915 is achieved for distinguishing between high- and low-quality segments. By removing low-quality segments, the average mIoU of the segmentation output is improved by 16 percentage points and the number of wrongly predicted segments is reduced by 77%.
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GLIMPSE: Holistic Cross-Modal Explainability for Large Vision-Language Models
A gradient-attention explainability method produces sequence-level visual and textual saliency maps for free-form answers from large vision-language models, with stronger human-attention alignment and faithfulness than prior baselines.