Mitigate Domain Shift by Primary-Auxiliary Objectives Association for
  Generalizing Person ReID
Qilei Li Shaogang Gong
While deep learning has significantly improved ReID model accuracy under the
independent and identical distribution (IID) assumption, it has also become
clear that such models degrade notably when applied to an unseen novel domain
due to unpredictable/unknown domain shift. Contemporary domain generalization
(DG) ReID models struggle in learning domain-invariant representation solely
through training on an instance classification objective. We consider that a
deep learning model is heavily influenced and therefore biased towards
domain-specific characteristics, e.g., background clutter, scale and viewpoint
variations, limiting the generalizability of the learned model, and hypothesize
that the pedestrians are domain invariant owning they share the same structural
characteristics. To enable the ReID model to be less domain-specific from these
pure pedestrians, we introduce a method that guides model learning of the
primary ReID instance classification objective by a concurrent auxiliary
learning objective on weakly labeled pedestrian saliency detection. To solve
the problem of conflicting optimization criteria in the model parameter space
between the two learning objectives, we introduce a Primary-Auxiliary
Objectives Association (PAOA) mechanism to calibrate the loss gradients of the
auxiliary task towards the primary learning task gradients. Benefiting from the
harmonious multitask learning design, our model can be extended with the recent
test-time diagram to form the PAOA+, which performs on-the-fly optimization
against the auxiliary objective in order to maximize the model's generative
capacity in the test target domain. Experiments demonstrate the superiority of
the proposed PAOA model.