Segment attention-guided part-aligned network for person re-identification

Part misalignment of the human body caused by complex variations in viewpoint and pose poses a fundamental challenge to person re-identification. This letter examines Res2Net as the backbone network to extract multi-scale appearance features. At the same time, it uses the human parsing model to extract part features, which can be used as an attention stream to guide part features re-calibration from the spatial dimension. Additionally, in order to ensure the diversity of features, SAG-PAN effectively integrates the global appearance features of person image with part fine-grained features. The experimental results on the Market-1501, DukeMTMC-reID and CUHK03 datasets show that the proposed SAG-PAN achieved superior performance against the existing state-of-the-art methods.