1703.In Defense of the Triplet Loss for Person Re-Identification 论文阅读笔记

时间 2021-01-05

标签行人重识别 reID 论文阅读笔记繁體版

原文原文链接

1703.In Defense of the Triplet Loss for Person Re-Identification-v2 本文为Triplet 的改进方法进行end-to-end 的基于深度学习的度量学习，作者改进了tripletloss，分别采用现成的可参数迁移的resnet-50v2模型和基于resnet设计的从头训练的模型进行了实验，在CUHK03，Market-1501和MA

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1. In Defense of the Triplet Loss for Person Re-Identification
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9. [论文笔记]Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning
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1. In Defense of the Triplet Loss for Person Re-Identification
2. 【行人重识别】《In Defense of the Triplet Loss for Person Re-Identification》论文阅读
3. 1705.IAN- The Individual Aggregation Network for Person Search 论文阅读笔记
4. 【TripletTracking(ECCV2018)】论文阅读 Triplet Loss in Siamese Network for Object Tracking
5. 1604.Joint Detection and Identification Feature Learning for Person Search论文阅读笔记
6. In Defense of Grid Features for Visual Question Answering论文阅读
7. 【论文阅读笔记】Towards Accurate Multi-person Pose Estimation in the Wild
8. Bag of Tricks and A Strong Baseline for Deep Person Re-identification（论文阅读笔记）
9. [论文笔记]Modeling Inter and Intra-Class Relations in the Triplet Loss for Zero-Shot Learning
10. 【论文阅读笔记】Vision-Based Fallen Person Detection for the Elderly

>>更多相关文章<<