R-FCN-3000 at 30fps: Decoupling Detection and Classification
論文地址：R-FCN-3000 at 30fps: Decoupling Detection and Classification 概述： 【導讀】美國馬里蘭大學、復旦大學和Gobasco人工智能實驗室聯合提出R-FCN-3000實時3000類目標檢測框架，對R-FCN框架中的物體檢測和分類進行解耦。本文對R-FCN體系結構進行修改，其中位置敏感濾波器在不同的目標類之間共享來進行定位。對於細粒度的
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相關文章
- 1. 【R-FCN-3000】《R-FCN-3000 at 30fps: Decoupling Detection and Classification》
- 2. 3000類目標檢測--R-FCN-3000 at 30fps: Decoupling Detection and Classification
- 3. 論文閱讀筆記（三十五）：R-FCN-3000 at 30fps: Decoupling Detection and Classification
- 4. 《R-FCN-3000 at 30fps：Decoupling Detection and Classification》論文筆記
- 5. 《Decoupling Representation and Classifier》筆記
- 6. (Review cs231n) Spatial Localization and Detection（classification and localization）
- 7. SemEval2019Task3_ERC | (2) Attentive Conversation Modeling for Emotion Detection and Classification
- 8. 論文：Automated Vehicle Detection and Classification: Models, Methods, and Techniques翻譯（三）
- 9. 論文：Automated Vehicle Detection and Classification: Models, Methods, and Techniques翻譯（五）
- 10. Comparison of SIFT Encoded and Deep Learning Features for the Classification and Detection of Esca D