  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.
