Adversarial Cross-Scale Alignment Pursuit For Seriously Misaligned Person Re-Identification

Person re-identification is still a challenging task in actual application due to serious misalignment caused by large scale variation, serious occlusion, or variably truncated body. Conventional holistic methods usually lack the cross-scale aligning ability. Segmentation-based partial methods achieve better aligning performance but generally suffer from the instability of part segmentation. To explicitly address these issues, we define the seriously misaligned Re-ID task and propose a novel framework called adversarial cross-scale alignment pursuit (ACSAP). Instead of dynamically segmenting the feature map for part alignment, the model incorporates the stability of holistic methods and adversarially generates aligned feature maps for similarity metrics. Especially, the spatial reconstruction (SR) module in the generator is proposed for feature filtering and aligning. Then a part visibility calculation (PVC) algorithm is proposed to distinguish the credibility of different generated areas. We propose a novel dataset called Seriously-Misaligned-REID and achieve 10.0% rank-1 outperformance compared to state-of-the-art methods on it. Extensive performance results demonstrate the effectiveness of our framework.