行人再识别：Joint Learning of Single-image and Cross-image Representations for Person Re-identification
摘要 行人再识别问题一般有两种解决思路，一种是对单张图像的特征进行匹配(SIR, single-image represention)，另一种是对交叉图像特征进行二分类处理(CIR,cross-image representation)。在本文中，我们对两种方式之间的联系进行了探索，并借助CNN提出了一种联合两种方式的学习框架。 对于上述的两种方式，第一种是通过比较两张图像特征之间的距离与阈值之间
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相关文章
- 1. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 论文笔记
- 2. Joint Discriminative and Generative Learning for Person Re-identification
- 3. (28)[AISTATS15] Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
- 4. Person search: Joint Detection and Identification Feature Learning for Person Search笔记
- 5. 行人再识别：Beyond triplet loss: a deep quadruplet network for person re-identification
- 6. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
- 7. Learning Generalisable Omni-Scale Representations for Person Re-Identification
- 8. 【论文笔记】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 9. Learning Delicate Local Representations for Multi-Person Pose Estimation
- 10. 自监督学习（四）Joint Unsupervised Learning of Deep Representations and Image Clusters