判别式
计算机科学
人工智能
特征(语言学)
任务(项目管理)
特征学习
机器学习
代表(政治)
秩(图论)
随机梯度下降算法
方案(数学)
梯度下降
鉴定(生物学)
特征向量
任务分析
绩效改进
模式识别(心理学)
特征提取
人工神经网络
数学
工程类
系统工程
法学
运营管理
哲学
语言学
数学分析
政治学
组合数学
政治
生物
植物
作者
Wei Long,Zhenyong Wei,Zhongming Jin,Zi‐Tao Yu,Jianqiang Huang,Deng Cai,Xiaofei He,Xian‐Sheng Hua
出处
期刊:IEEE transactions on image processing
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:29: 4942-4951
被引量:23
标识
DOI:10.1109/tip.2020.2975712
摘要
The re-identification (ReID) task has received increasing studies in recent years and its performance has gained significant improvement. The progress mainly comes from searching for new network structures to learn person representations. Most of these networks are trained using the classic stochastic gradient descent optimizer. However, limited efforts have been made to explore potential performance of existing ReID networks directly by better training scheme, which leaves a large space for ReID research. In this paper, we propose a Self-Inspirited Feature Learning (SIF) method to enhance performance of given ReID networks from the viewpoint of optimization. We design a simple adversarial learning scheme to encourage a network to learn more discriminative person representation. In our method, an auxiliary branch is added into the network only in the training stage, while the structure of the original network stays unchanged during the testing stage. In summary, SIF has three aspects of advantages: (1) it is designed under general setting; (2) it is compatible with many existing feature learning networks on the ReID task; (3) it is easy to implement and has steady performance. We evaluate the performance of SIF on three public ReID datasets: Market1501, DuckMTMC-reID, and CUHK03(both labeled and detected). The results demonstrate significant improvement in performance brought by SIF. We also apply SIF to obtain state-of-the-art results on all the three datasets. Specifically, mAP / Rank-1 accuracy are: 87.6% / 95.2% (without re-rank) on Market1501, 79.4% / 89.8% on DuckMTMC-reID, 77.0% / 79.5% on CUHK03 (labeled) and 73.9% / 76.6% on CUHK03 (detected), respectively. The code of SIF will be available soon.
科研通智能强力驱动
Strongly Powered by AbleSci AI