UAPAR is the first evidential deep learning framework for pedestrian attribute recognition that estimates attribute-wise epistemic uncertainty via a region-aware module and uses uncertainty-guided curriculum learning to handle label noise, achieving competitive results on PA100K, PETA, RAPv1 and RAP
Xinwen Fan, Yukang Zhang, Yang Lu, and Hanzi Wang
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cs.CV 2years
2026 2verdicts
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UECP replaces detection-correlated confidence maps with a LiDAR point-density uncertainty map and introduces Uncertainty-Aware Pyramid Fusion to improve collaborative perception.
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Uncertainty-Aware Pedestrian Attribute Recognition via Evidential Deep Learning
UAPAR is the first evidential deep learning framework for pedestrian attribute recognition that estimates attribute-wise epistemic uncertainty via a region-aware module and uses uncertainty-guided curriculum learning to handle label noise, achieving competitive results on PA100K, PETA, RAPv1 and RAP
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UECP: Uncertainty-Enhanced Collaborative Perception
UECP replaces detection-correlated confidence maps with a LiDAR point-density uncertainty map and introduces Uncertainty-Aware Pyramid Fusion to improve collaborative perception.