Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification(CVPR2019)閱讀筆記
1.摘要 在行人重識別問題裏，儘管局部特徵的方法有效，但是受限於需要成對的有標記的數據去訓練，這些數據往往難以獲得。在這項工作中，提出了一個基於塊的無監督學習框架用於學習易於區分的圖像塊特徵。設計了一個PatchNet用於從特徵圖中選取塊，從這些塊中學習易於區分的特徵。另外設計了一個圖像層面的特徵損失函數，利用同一張圖像的所有塊特徵合併爲一個圖像級別的嚮導用於PatchNet。 2.創新點 1)第
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相關文章
- 1. 【論文閱讀】 Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification
- 2. 【論文筆記】Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification
- 3. [CVPR2019]Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification論文筆記
- 4. 2019 CVPR：Patch-based Discriminative Feature Learning for Unsupervised Person Re-identification
- 5. Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification閱讀筆記
- 6. Learning a Discriminative Feature Network for Semantic Segmentation閱讀筆記
- 7. 1604.Joint Detection and Identification Feature Learning for Person Search論文閱讀筆記
- 8. 論文閱讀筆記：Unsupervised Feature Learning via Non-Parametric Instance Discrimination
- 9. Unsupervised Personalized Feature Selection--閱讀筆記
- 10. Joint Discriminative and Generative Learning for Person Re-identification