Deep Self-Taught Learning for Weakly Supervised Object Localization
種子樣本採集 1. Image-to-Object Transfer 訓練一個多標籤分類CNN模型作爲識別種子樣本的第一步，採用Hypothesis-CNN-Pooling (HCP)方法將圖像中屬於同一類物體的候選區域提取出來。在綜合預測階段對每一類採用cross-proposal max-pooling，篩選出一些與目標類最相關的候選區域。這樣，圖像級別的分類錯誤只能通過the most co
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相關文章
- 1. Deep Self-Taught Learning for Weakly Supervised Object Localization
- 2. 《Deep Self-Taught Learning for Weakly Supervised Object Localization》
- 3. Two-Phase Learning for Weakly Supervised Object Localization
- 4. [CVPR2017] Deep Self-Taught Learning for Weakly Supervised Object Localization 論文筆記
- 5. [CVPR2015] Is object localization for free? – Weakly-supervised learning with convolutional neural n...
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- 10. 【文獻閱讀】Zigzag Learning for Weakly Supervised Object Detection