多分支协作OSNet的微结构优化研究

On the Optimization of Multi-branch Cooperative OSNetfor Person Re-identification

OSNet是一种有效的轻量级行人重识别网络,因其兼具有轻量化和高性能的优异特点引起了行人重识别领域的关注。最近的研究表明:多分支协作OSNet网络——BC-OSNet能取得更高的识别率。本文在此基础上继续研究网络微结构的调整对BC-OSNet模型性能的影响,重点通过通用池化GeM、连续高斯Dropout、注意力学习Batch DropBlock(BDB)/Relation-Aware Global Attention(RGA)等微结构的有效融入,研究微结构优化的BC-OSNet性能提升效果。实验结果表明:经微结构优化的BC-OSNet在四个行人重识别数据集Market1501,Duke,CUHK03_Labeled和CUHK03_Detected上的mAP分别达到了89.9%,82.1%,84.2%和81.5%,相比初始的BC-OSNet提高0.6%,1.4%,1.1%和1.7%。

OSNet is an effective lightweight neural network architecture,which has attracted attention in the field of person re-identification duo to its excellent performance.Recently,we have proposed a multi-branch cooperative OSNet network based on OSNet,termed BC-OSNet,which performs significantly better than OSNet.In this paper,we study the further optimization of BC-OSNet with some adjustments on various micro-structures,including generalized-mean pooling,continuous Gaussian Dropout,attention modules of Batch DropBlock(BDB)/Relation-Aware Global Attention(RGA),etc.Experimental results show that the optimized BC-OSNet achieves 89.9%,82.1%,84.2%,and 81.5%mAP on the four pedestrian re-identification datasets,including Market1501,Duke,CUHK03_Labeled,and CUHK03_Detected,respectively.This means that the optimized BC-OSNet surpasses BC-OSNet about 0.6%,1.4%,1.1%and 1.7%in mAP for these datasets.