Neural Activation Constellations: Unsupervised Part Model Discovery with Convolutional Networks
本人是學生一枚，理解可能頗有偏頗~ 寫在前面：個人理解的一些比較關鍵的點 過程： 正常卷積，對某層產生P（通道數）個的激活圖求梯度得到P個特徵梯度圖，在此圖上找響應最強烈的位置, 作爲原圖中的關鍵點。關鍵點的個數等於P，從P個關鍵點中選出M個（可以隨機選擇，也可以用paper中的星座算法檢出率更高一些），以選出的關鍵點爲中心產生根據公式產生M個邊框（相當於窗）。之後對這些邊框中的圖像進行CNN分類
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相關文章
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