《Deep Self-Taught Learning for Weakly Supervised Object Localization》
本文提出依靠檢測器自身不斷改進訓練樣本質量，不斷增強檢測器性能的一種全新方法，破解弱監督目標檢測問題中訓練樣本質量低的瓶頸。 Weakly Supervised Objection Localization——弱監督目標定位 也就是說我們訓練的圖像只有針對圖像的【註釋】，沒有包含物體的矩形框，入論文中的圖片所示： 可以很顯然的看出，這種無矩形框的弱監督學習要比有矩形框的學習 難很多。 本文的算法流
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相關文章
- 1. Deep Self-Taught Learning for Weakly Supervised Object Localization
- 2. Two-Phase Learning for Weakly Supervised Object Localization
- 3. [CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 論文筆記
- 4. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n...
- 5. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n
- 6. Rethinking the Route Towards Weakly Supervised Object Localization筆記
- 7. 17-WILDCAT-Weakly-Supervised-Learning-of-DeepConvNets
- 8. Thinking in Weakly Supervised Learning
- 9. 【文獻閱讀】Zigzag Learning for Weakly Supervised Object Detection
- 10. Learning Deep Features for Discriminative Localization