Evidential Deep Learning

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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Uncertainty-Aware Pedestrian Attribute Recognition via Evidential Deep Learning
Apr 29, 2026Zhuofan Lou, Shihang Zhang, Fangle Zhu +2Evidential Deep Learning Epistemic Uncertainty
College of Electronics and Information Engineering, Sichuan University, China
UAU-Net: Uncertainty-aware Representation Learning and Evidential Classification for Facial Action Unit Detection
Apr 23, 2026Yuze Li, Zhilei LiuRepresentation Learning Evidential Deep Learning
School of Artificial Intelligence Tianjin University Tianjin, China
EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning
Apr 17, 2026Fangyuan Liu, Sirui Zhao, Yangsong Zhang +5Disentangled Representation Learning Evidential Deep Learning
School of Computer Science and Technology, University of Science and Technology of China, Hefei, China · aSchool of Computer Science and Technology, University of Science and Technology of China, Hefei, China · bSchool of Computer Science and Technology, Laboratory for Brain Science and Artificial Intelligence, Southwest University of Science and Technology, Mianyang, China +1
Evidential Perfusion Physics-Informed Neural Networks with Residual Uncertainty Quantification
Mar 10, 2026Junhyeok Lee, Minseo Choi, Han Jang +5Evidential Deep Learning Uncertainty Quantification
Cancer Biology, Seoul National University College of Medicine, Seoul, Korea · Radiology, Seoul National University Hospital, Seoul, Korea · Bioengineering, Seoul National University, Seoul, Korea +3
Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning
Feb 21, 2026Zhuofan Xie, Zishan Lin, Jinliang Lin +3Active Learning VLM Calibration
School of Electronic Technology and Engineering, Xiamen University, Xiamen, China · School of Informatics, Xiamen University, Xiamen, China · School of Engineering, Case Western Reserve University, Cleveland, USA
Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning
Feb 1, 2026Pietro Carlotti, Nevena Gligić, Arya FarahiEvidential Deep Learning OOD Detection
Department of Statistics and Data Sciences University of Texas at Austin
Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification
Date pendingYilin Zhang, Cai Xu, Haishun Chen +2Evidential Deep Learning Uncertainty Quantification
Xidian University Xi’an, China

Apr 29, 2026, cs.CVJ/K move · Enter open · S save

Uncertainty-Aware Pedestrian Attribute Recognition via Evidential Deep Learning

Zhuofan Lou, Shihang Zhang, Fangle Zhu, Shengjie Ye+1

College of Electronics and Information Engineering, Sichuan University, China

Evidential Deep Learning Epistemic Uncertainty

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We propose UAPAR, an Uncertainty-Aware Pedestrian Attribute Recognition framework. To the best of our knowledge, this is the first EDL-based uncertainty-aware framework for pedestrian attribute recognition (PAR). Unlike conventional deterministic methods, which fail to assess prediction reliability on low-quality samples, UAPAR effectively identifies unreliable predictions and thus enhances system robustness in complex real-world scenarios. To achieve this, UAPAR incorporates Evidential Deep Learning (EDL) into a CLIP-based architecture. Specifically, a Region-Aware Evidence Reasoning module employs cross-attention and spatial prior masks to capture fine-grained local features, which are further processed by an evidence head to estimate attribute-wise epistemic uncertainty. To further enhance training robustness, we develop an uncertainty-guided dual-stage curriculum learning strategy to alleviate the adverse effects of severe label noise during training. Extensive experiments on the PA100K, PETA, RAPv1, and RAPv2 datasets demonstrate that UAPAR achieves competitive or superior performance. Furthermore, qualitative results confirm that the proposed framework generates uncertainty estimates that are predictive of challenging or erroneous samples.

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