行人再識別：Joint Learning of Single-image and Cross-image Representations for Person Re-identification
摘要 行人再識別問題一般有兩種解決思路，一種是對單張圖像的特徵進行匹配(SIR, single-image represention)，另一種是對交叉圖像特徵進行二分類處理(CIR,cross-image representation)。在本文中，我們對兩種方式之間的聯繫進行了探索，並藉助CNN提出了一種聯合兩種方式的學習框架。 對於上述的兩種方式，第一種是通過比較兩張圖像特徵之間的距離與閾值之間
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相關文章
- 1. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 論文筆記
- 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筆記
- 5. 行人再識別：Beyond triplet loss: a deep quadruplet network for person re-identification
- 6. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 7. Learning Generalisable Omni-Scale Representations for Person Re-Identification
- 8. 【論文筆記】Joint Unsupervised Learning of Deep Representations and Image Clusters
- 9. Learning Delicate Local Representations for Multi-Person Pose Estimation
- 10. 自監督學習（四）Joint Unsupervised Learning of Deep Representations and Image Clusters