Person re-identification in the wild is often challenged by occlusion. Existing methods mainly rely on learned external cues like pose or parsing to ease occlusion distraction. This knowledge highly related to body semantics may introduce alignment effects, leading to additional requirements for dedicated training data and inference computation. We propose the Self-guided Body Part Alignment method that learns cue-free semantic-aligned local prediction for feature representations to avoid high-cost dependence on external cues. First, scale-wise global spatial attention is utilized to determine essential body parts automatically. A relation transformer network is then employed to predict semantic-aligned local parts, guided with anchored global information by constraint loss. Similarity metrics for all parts are merged with threshold conditions to filter invisible body parts comprehensively. Experimental results on occluded and holistic person reID benchmarks show the proposed method outperforms other cue-relied and cue-free methods. As far as we know, this is the first method that applies transformer networks on local predictions for occluded reID tasks.

中文翻译：

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自我引导的身体部位与关系转换器对齐，用于被遮挡的人重新识别

野外人员重新识别经常受到遮挡的挑战。现有方法主要依靠习得的外部线索（如姿势或解析）来缓解遮挡分心。这种与身体语义高度相关的知识可能会引入对齐效应，从而导致对专用训练数据和推理计算的额外要求。我们提出了自引导身体部位对齐方法，该方法学习无线索语义对齐的局部特征表示预测，以避免对外部线索的高成本依赖。首先，利用按比例缩放的全局空间注意力来自动确定基本的身体部位。然后使用关系转换器网络来预测语义对齐的局部部分，并通过约束损失以锚定的全局信息为指导。所有部位的相似性指标与阈值条件合并，以全面过滤不可见的身体部位。遮挡和整体行人 reID 基准的实验结果表明，所提出的方法优于其他依赖线索和无线索的方法。据我们所知，这是第一种将 Transformer 网络应用于局部预测的方法，用于被遮挡的 reID 任务。