论文标题
嫁接的人重新识别网络
Grafted network for person re-identification
论文作者
论文摘要
卷积神经网络在重新识别（RE-ID）方面表现出了出色的有效性。但是，这些模型始终具有大量参数和用于移动应用程序的大量计算。为了缓解这个问题，我们提出了一个新型的移植网络（GraftedNet），该网络是通过嫁接高准确的砧木和轻度加权的接替而设计的。砧木基于Resnet-50的前部部分，以提供强大的基线，而Scion是由Squeezenet后一个部分组成的新设计的模块，以压缩参数。为了提取更具歧视性特征表示，提出了联合多级和零件特征。此外，为了有效地训练移植网络，我们通过添加随附的分支来训练模型进行训练并将其删除，以在节省参数和计算中进行测试。在三个公众重新ID基准（Market1501，Dukemtmc-Reid和Cuhk03）上，评估了移植网络的有效性，并分析了其组件。实验结果表明，拟议的GraftedNet在Rank-1中获得93.02％，85.3％和76.2％，MAP中的MAP中达到了81.6％，74.7％和71.6％，只有460万参数。
Convolutional neural networks have shown outstanding effectiveness in person re-identification (re-ID). However, the models always have large number of parameters and much computation for mobile application. In order to relieve this problem, we propose a novel grafted network (GraftedNet), which is designed by grafting a high-accuracy rootstock and a light-weighted scion. The rootstock is based on the former parts of ResNet-50 to provide a strong baseline, while the scion is a new designed module, composed of the latter parts of SqueezeNet, to compress the parameters. To extract more discriminative feature representation, a joint multi-level and part-based feature is proposed. In addition, to train GraftedNet efficiently, we propose an accompanying learning method, by adding an accompanying branch to train the model in training and removing it in testing for saving parameters and computation. On three public person re-ID benchmarks (Market1501, DukeMTMC-reID and CUHK03), the effectiveness of GraftedNet are evaluated and its components are analyzed. Experimental results show that the proposed GraftedNet achieves 93.02%, 85.3% and 76.2% in Rank-1 and 81.6%, 74.7% and 71.6% in mAP, with only 4.6M parameters.
