Rethinking the Route Towards Weakly Supervised Object Localization筆記
CVPR2020 本文提出僞監督目標定位方法（PSOL）解決弱監督目標定位問題。將定位與分類分爲兩個獨立的網絡，在訓練集上使用DDT生成僞ground truth，通過迴歸與真實GT box對比， 整體效果達到SOTA。在不同數據集上，PSOL方法不需要fine-tuning也具有很好的定位能力，遷移能力。 由於訓練集數據大量標註困難，成本高。很多開始研究如何使用弱監督方法來學習 目標定位。弱監督
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相關文章
- 1. 弱監督目標檢測算法論文閱讀（二）Rethinking the Route Towards Weakly Supervised Object Localization
- 2. Two-Phase Learning for Weakly Supervised Object Localization
- 3. Deep Self-Taught Learning for Weakly Supervised Object Localization
- 4. 《Deep Self-Taught Learning for Weakly Supervised Object Localization》
- 5. [CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 論文筆記
- 6. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n...
- 7. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n
- 8. 論文報告筆記（四） Towards Precise End-to-end Weakly Supervised Object Detection Network
- 9. Generative Adversarial Learning Towards Fast Weakly Supervised Detection(CVPR2018)閱讀筆記
- 10. Weakly-Supervised Action Localization by Generative Attention Modeling批註