Adversarially Occluded Samples for Person Re-identification學習筆記
這是我學習ReID系列看的第二篇paper，這是今天做的筆記。 首先翻譯一下這篇文章的題目，在行人重檢測中使用對抗重疊的目標作爲樣本，讀起來可能比較拗口。簡單點說就是模擬生成將普通訓練的ReID模型中那些不能正常檢測出來的特殊的數據（這就是Adversarially Occluded Samples，博客中稱之爲錯誤樣本），將這些數據放入模型中進行訓練，可以提高模型的accuracy。 再介紹一下
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相關文章
- 1. 1707.Deep Learning for Person Reidentification Using Support Vector Machines 論文筆記
- 2. Pose-guided Visible Part Matching for Occluded Person ReID閱讀筆記
- 3. Pose-Guided Feature Alignment for Occluded Person Re-Identification閱讀筆記
- 4. person ReID（行人重識別）2018CVPR paper閱讀筆記
- 5. 【CVPR2020】：High-Order Information Matters: Learning Relation and Topology for Occluded Person Re-Ide
- 6. Person search: Joint Detection and Identification Feature Learning for Person Search筆記
- 7. 【閱讀筆記】Adversarially Regularized Autoencoders
- 8. 論文筆記 | Counterfactual Samples Synthesizing for Robust VQA
- 9. 論文閱讀《Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification》
- 10. Group Consistent Similarity Learning via Deep CRF for Person Re-Identification學習筆記