{
  "id": 7656,
  "url": "https://arxiv.org/abs/2603.05114v1",
  "title": "UniPAR: A Unified Framework for Pedestrian Attribute Recognition",
  "summary": "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",
  "authors": "Minghe Xu, Rouying Wu, Jiarui Xu, Minhao Sun, Zikang Yan, Xiao Wang et al.",
  "category": "research",
  "topics": "privacy-surveillance,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-05T12:34:35.000Z",
  "fetched_at": "2026-07-14T16:33:21.052Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7656",
  "original_url": "https://arxiv.org/abs/2603.05114v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}