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Exploit the Unknown Gradually: One-Shot Video-Based Person Re-Identification by Stepwise Learning
时间 2021-01-11
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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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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)
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