論文閱讀 Adversarial Complementary Learning forWeakly Supervised Object Localization
Abstract 文章提出了ACoL通過一種弱監督的方式自動定位整合的感興趣的區域，直接從最後的卷積層來選擇class-specific的feature map。其中整個網絡包含兩個分類器，其中一個分類器A用來定位discriminitive regions，而另一個分類器B用來定義A沒有定位出來的感興趣區域，從而形成一種互補。 任務定義 Weakly Supervised Object Loca
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相關文章
- 1. 論文閱讀：Dual Supervised Learning
- 2. 論文閱讀Soft Proposal Networks for weekly-supervised object localization
- 3. 論文閱讀-《Learning Deep Features for Discriminative Localization》
- 4. 《Deep Self-Taught Learning for Weakly Supervised Object Localization》
- 5. 論文筆記（八）：Object Instance Mining forWeakly Supervised Object Detection
- 6. [CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 論文筆記
- 7. 論文閱讀筆記 | (IJCAI 2018 Oral) Collaborative Learning for Weakly Supervised Object Detection
- 8. Deep Self-Taught Learning for Weakly Supervised Object Localization
- 9. 論文閱讀: 3D Human Pose Estimation in the Wild by Adversarial Learning
- 10. Two-Phase Learning for Weakly Supervised Object Localization