We are thrilled to announce our outstanding achievement, securing the 1st position in Track 1 of the UPAR Challenge at the WACV2024-RWS Workshop. Our accomplishment is attributed to the development of an advanced AI-based solution addressing the crucial task of pedestrian attribute recognition, with significant implications for applications such as tracking and retrieval.

Meet our dedicated team:

  1. Doanh C. Bui (Leader): Master’s student at the School of Electrical Engineering, Korea University
  2. Thinh V. Le: Undergraduate researcher at the University of Information Technology, VNU-HCM
  3. Hung Ba Ngo: Postdoctoral researcher at the Graduate School of Data Science, Chonnam National University

Furthermore, we are honored to share that our paper presenting this groundbreaking solution, titled “C2T-Net: Cross-Fused Transformer-Style Networks for Pedestrian Attribute Recognition,” has been accepted, and we will be delivering an oral presentation at the WACV2024-RWS Workshop. This recognition underscores the innovation and impact of our work in advancing the field of pedestrian attribute recognition.

Please check our published source code here.