Modality-aware collaborative learning for visible thermal person re-identification

Mang Ye, Xiangyuan Lan, Qingming Leng*

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

89 Citations (Scopus)


Visible thermal person re-identification (VT-ReID) is a cross-modality pedestrian retrieval problem, which automatically searches persons between day-time visible images and night-time thermal images. Despite the extensive progress in single-modality ReID, the cross-modality pedestrian retrieval problem has limited attention due to its challenges in modality discrepancy and large intra-class variations across cameras. Existing cross-modality ReID methods usually solve this problem by learning cross-modality feature representations with modality-sharable classifier. However, this learning strategy may lose discriminative information in different modalities. In this paper, we propose a novel modality-aware collaborative (MAC) learning method on top of a two-stream network for VT-ReID, which handles the modality-discrepancy in both feature level and classifier level. In feature level, it handles the modality discrepancy by a two-stream network with different parameters. In classifier level, it contains two separate modality-specific identity classifiers for two modalities to capture the modality-specific information, and they have the same network architecture but different parameters. In addition, we introduce a collaborative learning scheme, which regularizes the modality-sharable and modality-specific identity classifiers by utilizing the relationship between different classifiers. Extensive experiments on two cross-modality person re-identification datasets demonstrate the superiority of the proposed method, achieving much better performance than the state-of-the-art.

Original languageEnglish
Title of host publicationMM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450368896
Publication statusPublished - 15 Oct 2019
Event27th ACM International Conference on Multimedia, MM 2019 - Nice, France
Duration: 21 Oct 201925 Oct 2019 (Link to conference proceedings)

Publication series

NameMM 2019 - Proceedings of the 27th ACM International Conference on Multimedia


Conference27th ACM International Conference on Multimedia, MM 2019
Internet address

Scopus Subject Areas

  • Computer Science(all)
  • Media Technology

User-Defined Keywords

  • Collaborative Learning
  • Cross-modality
  • Pedestrian Retrieval
  • Person Re-Identification


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