论文解读：《Learning Generalisable Omni-Scale Representations for Person Re-Identification（OSNet）》
介绍 有效的行人再识别应该具有区分性，学习到的特征表示既能区分外貌相似的行人，又能在不额外训练的情况下进行跨数据集部署。本文提出了新的CNN架构来应对这两个挑战。首先，是提出了一种“全尺度网络（OSNet）”的CNN结构，不仅可以捕获不同的空间尺度，同时也能封装这些尺度的协同组合，即全尺度特征。基本构建块由多个卷积流组成，每个卷积流检测不同尺度范围的特征。对于全尺度特征学习，提出了一种统一的聚合门
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相关文章
- 1. Learning Generalisable Omni-Scale Representations for Person Re-Identification
- 2. 论文阅读：轻量级reid：Learning Generalisable Omni-Scale Representations for Person Re-Identification
- 3. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 论文笔记
- 4. part-aligned系列论文：1707.Deeply-Learned Part-Aligned Representations for Person Re-Identification 论文笔记
- 5. Learning Delicate Local Representations for Multi-Person Pose Estimation
- 6. 论文写作解读：A Simple Framework for Contrastive Learning of Visual Representations
- 7. 论文阅读：DeepWalk Online Learning of Social Representations
- 8. 论文阅读笔记《Learning Compositional Representations for Few-Shot Recognition》
- 9. 1607.CVPR-Joint Learning of Single-image and Cross-image Representations for Person ReID 论文笔记
- 10. Person Re-Identification by Deep Learning Multi-Scale Representations 论文笔记