AdaGPAR: Generalizable Pedestrian Attribute Recognition via Test-time Adaptation

Generalizable pedestrian attribute recognition (PAR) aims to learn a robust PAR model that can be directly adapted to unknown distributions under varying illumination, different viewpoints and occlusions, which is an essential problem for real-world applications, such as video surveillance and fashion search. In practice, when a trained PAR model is deployed to real-world scenarios, the unseen target samples are fed into the model continuously in an online manner. Therefore, this paper proposes an efficient and flexible method, named AdaGPAR, for generalizable PAR (GPAR) via test-time adaptation (TTA), where we adapt the trained model through exploiting the unlabeled target samples online during the test phase. As far as we know, it is the first work that solves the GPAR from the perspective of TTA. In particular, the proposed AdaGPAR memorizes the reliable target sample pairs (features and pseudo-labels) as prototypes gradually in the test phase. Then, it makes predictions with a non-parametric classifier by calculating the similarity between a target instance and the prototypes. However, since PAR is a task of multi-label classification, only using the same holistic feature of one pedestrian image as the prototypes of multiple attributes is not optimal. Therefore, an attribute localization branch is introduced to extract the attribute-specific features, where two kinds of memory banks are further constructed to cache the global and attribute-specific features simultaneously. In summary, the AdaGPAR is training-free in the test phase and predicts multiple pedestrian attributes of the target samples in an online manner. This makes the AdaGPAR time efficient and generalizable for real-world applications. Extensive experiments have been performed on the UPAR benchmark to compare the proposed method with multiple baselines. The superior performance demonstrates the effectiveness of the proposed AdaGPAR that improves the generalizability of a PAR model via TTA.