論文閱讀筆記 | (IJCAI 2018 Oral) Collaborative Learning for Weakly Supervised Object Detection
論文來自於上海交通大學，點擊下載網絡 Abstract 訓練一個高準確率的檢測模型須要大量精細標註的圖片數據，其成本很高。本文提出了一種弱監督協同窗習框架，僅使用粗略標籤的圖片訓練目標檢測模型，測試結果顯示其定位精確率和檢測準確率均顯著優於目前最早進的方法。app 本論文提出了一種弱監督協同窗習（WSCL）的框架，將弱監督學習網絡和強監督學習網絡鏈接成爲一個總體網絡，經過一致性損失（consist
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- 2. Collaborative Learning for Weakly Supervised Object Detection 算法筆記
- 3. 論文筆記（五） C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
- 4. 【文獻閱讀】Zigzag Learning for Weakly Supervised Object Detection
- 5. 筆記：論文閱讀Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
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- 7. 論文解讀：Object Instance Mining for Weakly Supervised Object Detection（AAAI 2020）
- 8. 論文筆記（六）MELM：Min-Entropy Latent Model for Weakly Supervised Object Detection
- 9. 論文筆記（七）泛讀 ：Weakly Supervised Region Proposal Network and Object Detection
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相關文章
- 1. 論文閱讀筆記 | (IJCAI 2018 Oral) Collaborative Learning for Weakly Supervised Object Detection
- 2. Collaborative Learning for Weakly Supervised Object Detection 算法筆記
- 3. 論文筆記（五） C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
- 4. 【文獻閱讀】Zigzag Learning for Weakly Supervised Object Detection
- 5. 筆記：論文閱讀Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation
- 6. Object Instance Mining for Weakly Supervised Object Detection 論文筆記
- 7. 論文解讀：Object Instance Mining for Weakly Supervised Object Detection（AAAI 2020）
- 8. 論文筆記（六）MELM：Min-Entropy Latent Model for Weakly Supervised Object Detection
- 9. 論文筆記（七）泛讀 ：Weakly Supervised Region Proposal Network and Object Detection
- 10. [CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 論文筆記