Person Re-identification based on Two-Stream Network with Attention and Pose Features 論文總結筆記
論文：基於注意力和姿勢特徵的雙流網絡行人重識別 文章目錄 1 摘要 2 理論研究 2.1 主網絡框架 2.2 特徵融合 2.2 self-attention 2.3 pose estimattion 2.4 Bilinear Pooling 3 Loss 1 摘要 由於姿態、模糊、遮擋等問題的存在，行人的重新識別(re-ID，Re-ID)目前仍是一項具有挑戰性的任務。本文結合了姿態估計和注意力機制
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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. Cross-Modality Person Re-Identification Based on Dual-Path Multi-Branch Network 論文閱讀筆記
- 3. Dual Attention Network for Scene Segmentation 論文筆記
- 4. 1606.End--to-End Comparative Attention Networks for Person Re-identification 論文筆記
- 5. MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network
- 6. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 7. 論文筆記：Cascaded Pyramid Network for Multi-Person Pose Estimation（CPN）
- 8. [論文閱讀]Learning Discriminative Features with Multiple Granularityfor Person Re-Identification
- 9. 《Real-time 2D Multi-Person Pose Estimation on CPU：Lightweight OpenPose》論文筆記
- 10. 論文閱讀筆記《leaning spatiotemporal features with 3D convolutional network》