Work

Intermediate domain-based unsupervised domain adaptive visual-infrared cross-modal person re-identification

Year: 2025

Type: conference-paper

Abstract: Visible-infrared cross-modal person re-identification aims at identifying the same person from images captured by non-overlapping cross-modal cameras, presenting a significant challenge in computer vision. Most existing methods only focus on the modality gap within a single domain, neglecting the domain gap between different domains, which negatively impacts performance when domains change. Therefore, we propose unsupervised domain adaptation approach. First, an intermediate domain generator is introduced to mix source and target domain representations. Second, a cross-modal clustering method is used to establish cross-modal correspondence in the target domain. Finally, weighted bipartite graph matching further aligns the visible and infrared modalities. To the best of our knowledge, this is the first attempt to apply unsupervised domain adaptation to visible-infrared cross-modal person re-identification. Extensive experimental on three benchmarks show that our method significantly improves generalization.

Source: International Conference on Computer Application and Information Security (ICCAIS)

DOI: 10.1117/12.3062363

Authors: Zhengchao Lei, Qi A, Shiqiang Chen, Sanyuan Zhao

Institutions: National Computer Network Emergency Response Technical Team/Coordination Center of Chinar, Beijing Institute of Technology

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4409784848

FWCI:

Cites: 24

Cited by:

Related to: 10