[paper reading] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detectio
MIL陷入局部最優，檢測到局部，沒法完整的檢測到物體。將instance劃分爲空間相關和類別相關的子集。在這些子集中定義一系列平滑的損失近似代替原損失函數，優化這些平滑損失。app
C-MIL learns instance subsets, where the instances are spatially related, i.e., overlapping with each other, and class related, i.e., having similar object class scores.函數
C-MIL treats images as bags and image regions generated by an object proposal method [24,32] as instances優化
待解決的問題：spa
1) How to optimize the non-convex function.net
2) How to perform instance selection in the early training stages when the instance selector is not well trained.3d
to be continue ...orm
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相關文章
- 1. 論文筆記（五） C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
- 2. 【paper reading】《Proposal learning for semi-supervised object detection》
- 3. Two-Phase Learning for Weakly Supervised Object Localization
- 4. Deep Self-Taught Learning for Weakly Supervised Object Localization
- 5. 《Deep Self-Taught Learning for Weakly Supervised Object Localization》
- 6. 論文解讀：Object Instance Mining for Weakly Supervised Object Detection（AAAI 2020）
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- 8. Object Instance Mining for Weakly Supervised Object Detection 論文筆記
- 9. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n...
- 10. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n