論文解讀：《Learning Generalisable Omni-Scale Representations for Person Re-Identification（OSNet）》
介紹 有效的行人再識別應該具有區分性，學習到的特徵表示既能區分外貌相似的行人，又能在不額外訓練的情況下進行跨數據集部署。本文提出了新的CNN架構來應對這兩個挑戰。首先，是提出了一種「全尺度網絡（OSNet）」的CNN結構，不僅可以捕獲不同的空間尺度，同時也能封裝這些尺度的協同組合，即全尺度特徵。基本構建塊由多個卷積流組成，每個卷積流檢測不同尺度範圍的特徵。對於全尺度特徵學習，提出了一種統一的聚合門
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相關文章
- 1. Learning Generalisable Omni-Scale Representations for Person Re-Identification
- 2. 論文閱讀：輕量級reid：Learning Generalisable Omni-Scale Representations for Person Re-Identification
- 3. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 4. part-aligned系列論文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 論文筆記
- 5. Learning Delicate Local Representations for Multi-Person Pose Estimation
- 6. 論文寫作解讀：A Simple Framework for Contrastive Learning of Visual Representations
- 7. 論文閱讀：DeepWalk Online Learning of Social Representations
- 8. 論文閱讀筆記《Learning Compositional Representations for Few-Shot Recognition》
- 9. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 論文筆記
- 10. Person Re-Identification by Deep Learning Multi-Scale Representations 論文筆記