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论文类型：会议论文

第一作者：Wang,Wang,Cheng

通讯作者：Wang,Xinggang

合写作者：Liu,Wenyu,Huang,Chang,Zhang,Qian

发表刊物：2018 European Conference on Computer Vision(ECCV)

发表时间：2018-09-08

摘要：We propose a novel deep network called Mancs that solves the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem and properly sampling for the ranking loss to obtain more stable person representation. Technically, we contribute a novel fully attentional block which is deeply supervised and can be plugged into any CNN, and a novel curriculum sampling method which is effective for training ranking losses. The learning tasks are integrated into a unified framework and jointly optimized. Experiments have been carried out on Market1501, CUHK03 and DukeMTMC. All the results show that Mancs can significantly outperform the previous state-of-the-arts. In addition, the effectiveness of the newly proposed ideas has been confirmed by extensive ablation studies.