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UniPAR: A Unified Framework for Pedestrian Attribute Recognition

Minghe Xu, Rouying Wu, Jiarui Xu, Minhao Sun, Zikang Yan, Xiao Wang, ChiaWei Chu, Yu Li

TL;DR

UniPAR is proposed, a unified Transformer-based framework for PAR that enables a single model to simultaneously process diverse datasets from heterogeneous modalities, including RGB images, video sequences, and event streams and introduces an innovative phased fusion encoder that explicitly aligns visual features with textual attribute queries through a late deep fusion strategy.

Abstract

Pedestrian Attribute Recognition is a foundational computer vision task that provides essential support for downstream applications, including person retrieval in video surveillance and intelligent retail analytics. However, existing research is frequently constrained by the ``one-model-per-dataset" paradigm and struggles to handle significant discrepancies across domains in terms of modalities, attribute definitions, and environmental scenarios. To address these challenges, we propose UniPAR, a unified Transformer-based framework for PAR. By incorporating a unified data scheduling strategy and a dynamic classification head, UniPAR enables a single model to simultaneously process diverse datasets from heterogeneous modalities, including RGB images, video sequences, and event streams. We also introduce an innovative phased fusion encoder that explicitly aligns visual features with textual attribute queries through a late deep fusion strategy. Experimental results on the widely used benchmark datasets, including MSP60K, DukeMTMC, and EventPAR, demonstrate that UniPAR achieves performance comparable to specialized SOTA methods. Furthermore, multi-dataset joint training significantly enhances the model's cross-domain generalization and recognition robustness in extreme environments characterized by low light and motion blur. The source code of this paper will be released on https://github.com/Event-AHU/OpenPAR

UniPAR: A Unified Framework for Pedestrian Attribute Recognition

TL;DR

UniPAR is proposed, a unified Transformer-based framework for PAR that enables a single model to simultaneously process diverse datasets from heterogeneous modalities, including RGB images, video sequences, and event streams and introduces an innovative phased fusion encoder that explicitly aligns visual features with textual attribute queries through a late deep fusion strategy.

Abstract

Pedestrian Attribute Recognition is a foundational computer vision task that provides essential support for downstream applications, including person retrieval in video surveillance and intelligent retail analytics. However, existing research is frequently constrained by the ``one-model-per-dataset" paradigm and struggles to handle significant discrepancies across domains in terms of modalities, attribute definitions, and environmental scenarios. To address these challenges, we propose UniPAR, a unified Transformer-based framework for PAR. By incorporating a unified data scheduling strategy and a dynamic classification head, UniPAR enables a single model to simultaneously process diverse datasets from heterogeneous modalities, including RGB images, video sequences, and event streams. We also introduce an innovative phased fusion encoder that explicitly aligns visual features with textual attribute queries through a late deep fusion strategy. Experimental results on the widely used benchmark datasets, including MSP60K, DukeMTMC, and EventPAR, demonstrate that UniPAR achieves performance comparable to specialized SOTA methods. Furthermore, multi-dataset joint training significantly enhances the model's cross-domain generalization and recognition robustness in extreme environments characterized by low light and motion blur. The source code of this paper will be released on https://github.com/Event-AHU/OpenPAR
Paper Structure (18 sections, 5 equations, 4 figures, 6 tables)

This paper contains 18 sections, 5 equations, 4 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Pedestrian Attribute Recognition
  4. Unified Modelling
  5. Methodology
  6. Overview
  7. Multi-modal Visual Embedding
  8. Unified Data Scheduling Strategy
  9. Objective Function
  10. Experiments
  11. Datasets and Evaluation Metric
  12. Implementation Details
  13. Comparison on Public Benchmarks
  14. Ablation Study
  15. Parameter Analysis
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Comparison between existing PAR models and our newly proposed one.
  • Figure 2: The overall architecture of our proposed UnifiedPAR framework
  • Figure 3: Visual Analysis of the Generalization Performance of Our Proposed Model on the MSP60k, DUKE, and EventPAR Datasets. Red markers indicate misclassified attributes, while orange markers denote undetected attributes.
  • Figure 4: Visualizing the Cross-Dataset Generalization of Our Proposed Model. Red markers indicate misclassified attributes, while orange markers denote undetected attributes.