Indexed by:会议论文
First Author:Wang,Wang,Cheng
Correspondence Author:Wang,Xinggang
Co-author:Liu,Wenyu,Huang,Chang,Zhang,Qian
Journal:2018 European Conference on Computer Vision(ECCV)
Date of Publication:2018-09-08
Abstract: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.