  Visible-infrared person re-identification (VI-ReID) has been challenging due
to the existence of large discrepancies between visible and infrared
modalities. Most pioneering approaches reduce intra-class variations and
inter-modality discrepancies by learning modality-shared and ID-related
features. However, an explicit modality-shared cue, i.e., body keypoints, has
not been fully exploited in VI-ReID. Additionally, existing feature learning
paradigms imposed constraints on either global features or partitioned feature
stripes, which neglect the prediction consistency of global and part features.
To address the above problems, we exploit Pose Estimation as an auxiliary
learning task to assist the VI-ReID task in an end-to-end framework. By jointly
training these two tasks in a mutually beneficial manner, our model learns
higher quality modality-shared and ID-related features. On top of it, the
learnings of global features and local features are seamlessly synchronized by
Hierarchical Feature Constraint (HFC), where the former supervises the latter
using the knowledge distillation strategy. Experimental results on two
benchmark VI-ReID datasets show that the proposed method consistently improves
state-of-the-art methods by significant margins. Specifically, our method
achieves nearly 20$\%$ mAP improvements against the state-of-the-art method on
the RegDB dataset. Our intriguing findings highlight the usage of auxiliary
task learning in VI-ReID.
