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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

Feature aggregation cross-modality person re-identification method driven by frequency domain spatial information

Jin Jing
Zhu Chuanbin
Zhai Fengwen
Lanzhou Jiaotong University, School of Electronic & Information Engineering, Lanzhou Gansu 730070, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0143

Abstract

The cross-modality person re-identification aims to match person images under different modalities of visible and infrared. The core challenge of this task is to alleviate the differences between visible and infrared images and extract discriminative shared features. However, existing methods fail to fully utilize modality information after data augmentation and overlook the semantic correlation between features at different scales while minimizing modality differences and extracting modality-shared features. This paper proposed a Frequency Domain Spatial Information Feature Aggregation (FDSIFA) network. First, it designed a multi-branch frequency-spatial perception module (MFSPM) to fully extract modality-specific information from both augmented and original images, while exploring cross-modality feature consistency in both frequency and spatial dimensions, effectively reducing modality differences. Then, it designed a Multi-stage Feature Aggregation Module (MFAM) to adaptively fuse features at different scales, explore the semantic relationships between low-level and high-level features, and enhance semantic representation and discriminability. The proposed network achieved Rank-1 accuracy of 75.09% and mAP of 71.35% in the all-search mode on the SYSU-MM01 dataset, outperforming existing comparison methods. The experimental results confirm the effectiveness of the proposed approach.

Foundation Support

甘肃省高校教师创新基金项目(2025B-060)
宁夏自然科学基金资助项目(2023AAC03714)
甘肃省科技计划项目重点研发计划-工业类(23YFGA0047)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0143
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-06] Accepted Paper

Cite This Article

金静, 朱传斌, 翟凤文. 频域空间信息驱动的特征聚合跨模态行人重识别方法 [J]. 计算机应用研究, 2025, 42 (12). (2025-08-06). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0143. (Jin Jing, Zhu Chuanbin, Zhai Fengwen. Feature aggregation cross-modality person re-identification method driven by frequency domain spatial information [J]. Application Research of Computers, 2025, 42 (12). (2025-08-06). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0143. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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