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Article
PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search
arXiv
  • Mustansar Fiaz, Mohamed bin Zayed University of Artificial Intelligence
  • Hisham Cholakkal, Mohamed bin Zayed University of Artificial Intelligence
  • Sanath Narayan, Inception Institute of Artificial Intelligence
  • Rao Anwer, Mohamed bin Zayed University of Artificial Intelligence
  • Fahad Shahbaz Khan, Mohamed bin Zayed University of Artificial Intelligence
Document Type
Article
Abstract

Person search is a challenging problem with various real-world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery images. Although, previous study focuses on rich feature information learning, it’s still hard to retrieve the query person due to the occurrence of appearance deformations and background distractors. In this paper, we propose a novel attention-aware relation mixer (ARM) module for person search, which exploits the global relation between different local regions within RoI of a person and make it robust against various appearance deformations and occlusion. The proposed ARM is composed of a relation mixer block and a spatio-channel attention layer. The relation mixer block introduces a spatially attended spatial mixing and a channel-wise attended channel mixing for effectively capturing discriminative relation features within an RoI. These discriminative relation features are further enriched by introducing a spatio-channel attention where the foreground and background discriminability is empowered in a joint spatio-channel space. Our ARM module is generic and it does not rely on fine-grained supervisions or topological assumptions, hence being easily integrated into any Faster R-CNN based person search methods. Comprehensive experiments are performed on two challenging benchmark datasets: CUHK-SYSU and PRW. Our PS-ARM achieves state-of-the-art performance on both datasets. On the challenging PRW dataset, our PS-ARM achieves an absolute gain of 5% in the mAP score over SeqNet, while operating at a comparable speed. The source code and pre-trained models are available at (this https URL). © 2022, CC BY.

DOI
10.48550/arXiv.2210.03433
Publication Date
10-7-2022
Keywords
  • channel attention,
  • Person Search,
  • Spatial attention,
  • Transformer
Comments

Preprint: arXiv

Archived with thanks to arXiv

Preprint License: CC by 4.0

Uploaded 31 October 2022

Citation Information
M. Fiaz, H. Cholakkal, S. Narayan, R. M. Anwer, and F.S. Khan, "PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search, 2022, doi: 10.48550/arXiv.2210.03433