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Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-ID
时间 2020-12-30
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局部可见感知模型——VPM 论文摘要 论文中考虑了行人重新识别 (re-ID) 的一个现实问题,即局部行人重识别问题。在局部行人重识别场景下,需要探索数据库图像中包含整体人物的比例大小。如果直接将局部行人图像与整体行人图像进行比较,则极端空间错位问题将显著地损害特征表征学习的判别能力。针对这个问题,本文提出了一个局部可见感知模型 (VPM),通过自监督学习感知可见区域内的特征,来解决局部行人重识别
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相关文章
1.
Perceive Where to Focus: Learning Visibility-aware Part-level Features for Partial Person Re-id
2.
【ReID】Learning Discriminative Features with Multiple Granularities for Person Re-identification
3.
【ReID】Hierarchical and Efficient Learning for Person Re-Identification
4.
《Learning Part-based Convolutional Features for Person Re-identification》TPAMI2019
5.
CVPR2019行人重识别论文笔记1904.00537:Learning Visibility-aware Part-level Features for Partial Person Re-id
6.
【Person Re-ID】Deep-Person: Learning Discriminative Deep Features for Person Re-Identification
7.
VPM 行人局部特征可见感知学习ReID
8.
Person Re-Identification (ReID), a New Facial Recognition Record
9.
【ReID】【Skimming】Unsupervised Cross-Dataset Transfer Learning for Person Re-identification
10.
ReID简记-3-3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-id
>>更多相关文章<<