论文标题
人重新识别的异质分支和多层次分类网络
A heterogeneous branch and multi-level classification network for person re-identification
论文作者
论文摘要
最近已证明具有多个分支的卷积神经网络在人体重新识别（RE-ID）中非常有效。研究人员使用零件模型设计了多分支网络，但它们总是将有效性归因于多个部分。此外，现有的多分支网络始终具有缺乏结构多样性的同构分支。为了改善这个问题，我们提出了一个新型的异质分支和多级分类网络（HBMCN），该分类网络是基于预先训练的Resnet-50模型而设计的。提出了一个新的异质分支，即SE-RES-BRANCH，该分支是基于SE-RES模块的，该模块由挤压和兴奋块和残留块组成。此外，提出了一种新的多级分类关节目标函数，以进行HBMCN的监督学习，从而从多个高级层中提取多层次特征并串联以代表一个人。基于三个公众RE-ID基准测试（Market1501，Dukemtmc-Reid和Cuhk03），实验结果表明，拟议的HBMCN在排名-1中达到94.4％，85.7％和73.8％，在MAP中，在MAP中达到85.7％，74.6％和69.0％，在MAP中达到了态度的绩效。进一步的分析表明，特殊设计的异质分支的表现比同构分支更好，并且多级分类提供了与单层分类相比的更具歧视性特征。结果，HBMCN对人重新ID任务提供了实质性的进一步改进。
Convolutional neural networks with multiple branches have recently been proved highly effective in person re-identification (re-ID). Researchers design multi-branch networks using part models, yet they always attribute the effectiveness to multiple parts. In addition, existing multi-branch networks always have isomorphic branches, which lack structural diversity. In order to improve this problem, we propose a novel Heterogeneous Branch and Multi-level Classification Network (HBMCN), which is designed based on the pre-trained ResNet-50 model. A new heterogeneous branch, SE-Res-Branch, is proposed based on the SE-Res module, which consists of the Squeeze-and-Excitation block and the residual block. Furthermore, a new multi-level classification joint objective function is proposed for the supervised learning of HBMCN, whereby multi-level features are extracted from multiple high-level layers and concatenated to represent a person. Based on three public person re-ID benchmarks (Market1501, DukeMTMC-reID and CUHK03), experimental results show that the proposed HBMCN reaches 94.4%, 85.7% and 73.8% in Rank-1, and 85.7%, 74.6% and 69.0% in mAP, achieving a state-of-the-art performance. Further analysis demonstrates that the specially designed heterogeneous branch performs better than an isomorphic branch, and multi-level classification provides more discriminative features compared to single-level classification. As a result, HBMCN provides substantial further improvements in person re-ID tasks.
