1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 論文筆記
作者將reid任務按照最終測度方法的不同，分成了兩類：即按照距離loss訓練方法的SIR（single-image representation）和按照對比loss訓練網絡的CIR（cross-image representation），作者提出了一種卷積神經網絡框架，可以將兩類方法整合到一個框架裏，進行聯合訓練，最終以得分和閾值設置的形式應用訓練好的模型。這樣可以提升匹配率的直覺上的原因： SI
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相關文章
- 1. ReID論文筆記1-Deeply-Learned Part-Aligned Representations for Person Re-Identification
- 2. 2020Arxiv之ReID：Deep Learning for Person Re-identification: A Survey and Outlook論文筆記
- 3. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 4. 【ReID】Hierarchical and Efficient Learning for Person Re-Identification
- 5. part-aligned系列論文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 論文筆記
- 6. 論文筆記----DeepWalk: Online Learning of Social Representations
- 7. 論文筆記：DeepWalk: Online Learning of Social Representations
- 8. ReID簡記-3-3D PersonVLAD: Learning Deep Global Representations for Video-based Person Re-id
- 9. 【論文筆記】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 10. 1610.Deep Learning Prototype Domains for Person Re-Identification-CUHK-SYSU-PRW 論文筆記