Deeply-Learned Part-Aligned Representations for Person Re-Identification 論文筆記
一、提出問題 本文主要針對行人重識別過程中的身體部位錯位匹配導致識別精度有限的問題進行研究。 由圖1-1可以看出行人姿勢多變，人體空間分佈魯棒性低，而且現有空間分割算法建立在人體空間分佈大致相同的假設下，但這種假設並不總符合實際情況。 圖1-1 身體部位對齊的必要性 二、論文貢獻 提出了一種深度神經網絡方法，它聯合身體部位特徵提取和特徵計算，無需標記有關人體部位的信息，而是通過端到端方式最大化
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相關文章
- 1. part-aligned系列論文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 論文筆記
- 2. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 3. Part-Aligned Bilinear Representations for Person Re-identification 論文筆記
- 4. ReID論文筆記1-Deeply-Learned Part-Aligned Representations for Person Re-Identification
- 5. 論文筆記：Deeply-Learned Part-Aligned Representations for Person Re-Identification
- 6. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 論文筆記
- 7. 1705.IAN- The Individual Aggregation Network for Person Search 論文閱讀筆記
- 8. Person Re-Identification by Deep Learning Multi-Scale Representations 論文筆記
- 9. 1505.Deep Ranking for Person Re-identification via Joint Representation Learning論文筆記
- 10. 1610.Deep Learning Prototype Domains for Person Re-Identification-CUHK-SYSU-PRW 論文筆記