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Dynamic Dual-Attentive Aggregation Learning for Visible-Infrared Person Re-identification

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12362)

Abstract

Visible-infrared person re-identification (VI-ReID) is a challenging cross-modality pedestrian retrieval problem. Due to the large intra-class variations and cross-modality discrepancy with large amount of sample noise, it is difficult to learn discriminative part features. Existing VI-ReID methods instead tend to learn global representations, which have limited discriminability and weak robustness to noisy images. In this paper, we propose a novel dynamic dual-attentive aggregation (DDAG) learning method by mining both intra-modality part-level and cross-modality graph-level contextual cues for VI-ReID. We propose an intra-modality weighted-part attention module to extract discriminative part-aggregated features, by imposing the domain knowledge on the part relationship mining. To enhance robustness against noisy samples, we introduce cross-modality graph structured attention to reinforce the representation with the contextual relations across the two modalities. We also develop a parameter-free dynamic dual aggregation learning strategy to adaptively integrate the two components in a progressive joint training manner. Extensive experiments demonstrate that DDAG outperforms the state-of-the-art methods under various settings.

Keywords

Person re-identification Graph attention Cross-modality 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Inception Institute of Artificial IntelligenceAbu DhabiUAE
  2. 2.Indiana UniversityBloomingtonUSA
  3. 3.University of RochesterRochesterUSA
  4. 4.Mohamed bin Zayed University of Artificial IntelligenceAbu DhabiUAE

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