Class-Weighted Convolutional Features for Visual Instance Search
Class-Weighted Convolutional Features for Visual Instance Search 文中的主要內容是，從feature map中根據圖像目標找到典型激活區域。這種方式是基於CAMs的方式。 在re-ranking階段使用acms形成region proposal。 使用CAMS 形成語義權重，來進行全集層的特徵抽取。 文中的主要
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相關文章
- 1. 轉載圖像檢索Class-Weighted Convolutional Features for Visual Instance Search
- 2. Faster R-CNN Features for Instance Search
- 3. Bags of Local Convolutional Features for Scalable Instance Search 論文解讀
- 4. Bags of Local Convolutional Features for Scalable Instance Search論文翻譯
- 5. 論文解讀 Saliency Weighted Convolutional Features for Instance Search
- 6. Faster R-CNN Features for Instance Search 的閱讀筆記
- 7. Object tracking papers (1): SINT--Siamese Instance Search for Tracking
- 8. CREST: Convolutional Residual Learning for Visual Tracking
- 9. [解讀] Deep Clustering for Unsupervised Learning of Visual Features
- 10. Richer Convolutional Features for Edge Detection論文筆記