Person Re-ID is challenged by background clutter, body misalignment and part missing. In this paper, we propose a reliable part-based multiple levels attention deep network to learn multiple scales salience representation. In particular, person alignment and key point detection are sequentially carried out to locate three relative stable body components, then fused attention (FA) mode is designed to capture the fine-grained salient features from effective spatial of valuable channels of each part, regional attention mode is succeeded to weight the importance of different parts for highlighting the representative parts while suppressing the valueless ones. A late fusion-based multiple-task loss is finally adopted to further optimize the valuable feature representation. Experimental results demonstrate that the proposed method achieves state-of-the-art performances on three challenging benchmarks: Market-1501, DukeMTMC-reID and CUHK03.

中文翻译：

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用于人员重新识别的可靠部分引导的多层次注意学习

Person Re-ID 面临背景杂乱、身体错位和部分缺失的挑战。在本文中，我们提出了一种可靠的基于零件的多层次注意力深度网络来学习多尺度显着性表示。特别是依次进行人位对齐和关键点检测，定位三个相对稳定的身体部位，然后设计融合注意力（Fused attention，FA）模式，从每个部位的有价值通道的有效空间中捕捉细粒度的显着特征，区域注意力模式成功地对不同部分的重要性进行加权，以突出代表部分，同时抑制无价值的部分。最后采用基于后期融合的多任务损失来进一步优化有价值的特征表示。