Pedestrian Attribute Recognition

Latest papers

9 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

LENS: Likelihood Evaluation and Neighbor Smoothing for Attribute-Based Person Retrieval on Unseen Cameras
Oct 24, 2026Nguyen Dang Duy Tan, Vo Thanh Nguyen, Linh T. T. Tran +1Person Re-Identification Retrieval Robustness
AI VIET NAM · Ho Chi Minh City University of Technology and Engineering · University of Information Technology, VNU-HCM +1
UniQuery: Unifying Vision Models with a Joint Query Head for Cross-Domain Pedestrian Attribute Recognition
Oct 24, 2026Nguyen Dang Duy Tan, Linh T. T. Tran, Huynh Nguyen Phat +1Pedestrian Attribute Recognition Feature Fusion
AI VIET NAM · University of Information Technology, VNU-HCM · University of Science, VNU-HCM +1
Real-time pedestrian attribute recognition with YOLOv8 and ResNet18
Jun 19, 2026Houssam El MirPedestrian Attribute Recognition
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, China
An Empirical Analysis of Optimization Dynamics and Sparsity Boundaries in Large-Scale Pedestrian Attribute Recognition
Jun 9, 2026Houssam El MirClass-Imbalanced Learning Focal Loss
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China
ReSAGE-PAR: Representational Similarity Assessment for Generative Expansion in Pedestrian Attribute Recognition
Jun 4, 2026Pablo Ayuso-Albizu, Pablo Carballeira, Juan C. SanMiguel +1Synthetic Data Augmentation Pedestrian Attribute Recognition
Universidad Aut´onoma de Madrid, Madrid, Spain
Uncertainty-Aware Pedestrian Attribute Recognition via Evidential Deep Learning
Apr 29, 2026Zhuofan Lou, Shihang Zhang, Fangle Zhu +2Evidential Deep Learning Pedestrian Attribute Recognition
College of Electronics and Information Engineering, Sichuan University, China
PFM-VEPAR: Prompting Foundation Models for RGB-Event Camera based Pedestrian Attribute Recognition
Mar 20, 2026Minghe Xu, Rouying Wu, ChiaWei Chu +2Pedestrian Attribute Recognition Event-Based Vision
City University of Macau, Macau SAR, China · Zhuhai College of Science and Technology, Zhuhai, China · Macau University of Science and Technology, Macau SAR, China +1
UniPAR: A Unified Framework for Pedestrian Attribute Recognition
Mar 5, 2026Minghe Xu, Rouying Wu, Jiarui Xu +5Pedestrian Attribute Recognition Unified Multimodal Models
Faculty of Data Science, City University of Macau, Macau SAR, China · School of Big Data, Zhuhai College of Science and Technology, Zhuhai 519041, China · Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR, China +1
Open-Attribute Person Retrieval: Finding People Through Distinctive and Novel Attributes
Aug 2, 2025Minjeong Park, Hongbeen Park, Sangwon Lee +1Pedestrian Attribute Recognition Information Retrieval
The Pennsylvania State University · Korea Telecom Research · Korea University +1

Oct 24, 2026J/K move · Enter open · S save

LENS: Likelihood Evaluation and Neighbor Smoothing for Attribute-Based Person Retrieval on Unseen Cameras

Nguyen Dang Duy Tan, Vo Thanh Nguyen, Linh T. T. Tran, Huynh Nguyen Phat

AI VIET NAM · Ho Chi Minh City University of Technology and Engineering · University of Information Technology, VNU-HCM+1

Person Re-Identification Retrieval Robustness Pedestrian Attribute Recognition

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Attribute-based person retrieval ranks surveillance images from a binary query of semantic attributes, but mean Average Attribute Degree of Match (mADM) drops on unseen cameras as predicted probabilities shift. Common pipelines rank gallery images by their L1 distance to the query, which penalizes a confident single-attribute mismatch no more than small uncertainties across many attributes, while post-hoc recalibration fitted on source validation data fails to transfer because the per-attribute maps fitted on source data do not match those of new cameras. We propose LENS (Likelihood Evaluation and Neighbor Smoothing), combining fine-tuned DINOv3 and CLIP models with a distilled student. LENS regularizes mutually exclusive attribute groups with group cross-entropy in training, scores gallery images by the log-likelihood of the exact query, and smooths gallery logits over nearest neighbors using DINOv3 appearance features from the same forward pass. On held-out datasets, all three changes raise mADM: the likelihood score improves all six retrieval metrics for the main ensemble and mADM for all 48 development models, and training-free kNN smoothing improves all 46 tested development models. On the hidden test set of the UPAR 2027 benchmark, LENS reaches 68.17 mADM.