Person Re-Identification by Deep Learning Multi-Scale Representations 論文筆記
Person Re-Identification by Deep Learning Multi-Scale Representations 論文筆記 一、提出問題 現有的re-id方法主要依賴於單一尺度的外觀信息，但是這不僅會忽略了其他不同尺度的潛在有用的顯性信息，而且還失去了跨尺度挖掘隱式相關互補的優勢。 在不同尺度上的特徵學習可能不同甚至相互不一致，因此多尺度的直接特徵連接不可能導致最優特徵融
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相關文章
- 1. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 2. part-aligned系列論文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 論文筆記
- 3. 【論文筆記——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 論文閱讀筆記
- 6. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 7. 1610.Deep Learning Prototype Domains for Person Re-Identification-CUHK-SYSU-PRW 論文筆記
- 8. 1705.Person Re-Identification by Deep Joint Learning of Multi-Loss Classification 論文閱讀筆記
- 9. 【論文筆記】Improving Person Re-identification by Attribute and Identity Learning
- 10. 論文筆記(1)：Deep Learning.