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While deep learning has significantly improved ReID model accuracy under theindependent and identical distribution (IID) assumption, it has also becomeclear that such models degrade notably when applied to an unseen novel domaindue to unpredictable/unknown domain shift. Contemporary domain generalization(DG) ReID models struggle in learning domain-invariant representation solelythrough training on an instance classification objective. We consider that adeep learning model is heavily influenced and therefore biased towardsdomain-specific characteristics, e.g., background clutter, scale and viewpointvariations, limiting the generalizability of the learned model, and hypothesizethat the pedestrians are domain invariant owning they share the same structuralcharacteristics. To enable the ReID model to be less domain-specific from thesepure pedestrians, we introduce a method that guides model learning of theprimary ReID instance classification objective by a concurrent auxiliarylearning objective on weakly labeled pedestrian saliency detection. To solvethe problem of conflicting optimization criteria in the model parameter spacebetween the two learning objectives, we introduce a Primary-AuxiliaryObjectives Association (PAOA) mechanism to calibrate the loss gradients of theauxiliary task towards the primary learning task gradients. Benefiting from theharmonious multitask learning design, our model can be extended with the recenttest-time diagram to form the PAOA+, which performs on-the-fly optimizationagainst the auxiliary objective in order to maximize the model's generativecapacity in the test target domain. Experiments demonstrate the superiority ofthe proposed PAOA model.

Mitigate Domain Shift by Primary-Auxiliary Objectives Association for Generalizing Person ReID