Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning
在这篇论文中，作者提出了通过逐渐利用未标注样本，来解决单标注样本（one-shot）情况下的视频行人重识别问题（video-based person re-ID）。这个方法简单通用，在MARS和DukeMTMC两个视频行人重识别数据集上都达到了远超 state-of-the art 的性能。 1. 论文概述 为什么需要关注单标注（one shot)样本问题？ 目前大多行人重识别方法都依赖于完全的数
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- 1. 【EUG】Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learn
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相关文章
- 1. 【EUG】Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learn
- 2. CVPR 2018 行人重识别：Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by S L
- 3. Stepwise Metric Promotion for Unsupervised Video Person Re-identification
- 4. 【FSR】Feature Space Regularization for Person Re-Identification with One Sample
- 5. Unsupervised Person Re-identification by Soft Multilabel Learning
- 6. Improving Person Re-identification by Attribute and Identity Learning
- 7. Unsupervised Person Re-identification by Deep Learning Tracklet Association
- 8. Person search: Joint Detection and Identification Feature Learning for Person Search笔记
- 9. 【Person Re-ID】Person Re-Identification by Deep Learning Multi-Scale Representations
- 10. OSCP Learning Notes - Exploit(6)