Person Re-Identification by Deep Learning Multi-Scale Representations 论文笔记
Person Re-Identification by Deep Learning Multi-Scale Representations 论文笔记 一、提出问题 现有的re-id方法主要依赖于单一尺度的外观信息，但是这不仅会忽略了其他不同尺度的潜在有用的显性信息，而且还失去了跨尺度挖掘隐式相关互补的优势。 在不同尺度上的特征学习可能不同甚至相互不一致，因此多尺度的直接特征连接不可能导致最优特征融
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相关文章
- 1. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
- 2. part-aligned系列论文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 论文笔记
- 3. 【论文笔记——DIM】Learning Deep Representations By Mutual Information Estimation and Maximization
- 4. 【Person Re-ID】Person Re-Identification by Deep Learning Multi-Scale Representations
- 5. Machine Learning & Deep Learning 论文阅读笔记
- 6. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning论文笔记
- 7. 1610.Deep Learning Prototype Domains for Person Re-Identification-CUHK-SYSU-PRW 论文笔记
- 8. 1705.Person Re-Identification by Deep Joint Learning of Multi-Loss Classification 论文阅读笔记
- 9. 【论文笔记】Improving Person Re-identification by Attribute and Identity Learning
- 10. 论文笔记(1)：Deep Learning.