Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Casca
參考：https://blog.csdn.net/bea_tree/article/details/51880175 Abstract 本文的兩個創新點： (1)SDP:scale-dependent pooling,根據candinate object proposals的尺度，去不同的特徵層上來提取特徵。類似SSD (2)CRC:cascaded rejection classifers
相關文章
- 1. Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
- 2. 論文SDP + RCNN | Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
- 3. Semantic Flow for Fast and Accurate Scene Parsing
- 4. Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo
- 5. [論文解讀]Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty
- 6. 論文：Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo
- 7. Fast and accurate short read alignment with Burrows-Wheeler transform
- 8. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
- 9. Scale-Transferrable Object Detection
- 10. CNN 1.9 Pooling Layers
- 更多相關文章...
- • XSL-FO table-and-caption 對象 - XSL-FO 教程
- • W3C RDF and OWL 活動 - W3C 教程
- • RxJava操作符（七）Conditional and Boolean
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
- 2. 論文SDP + RCNN | Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
- 3. Semantic Flow for Fast and Accurate Scene Parsing
- 4. Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo
- 5. [論文解讀]Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty
- 6. 論文：Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo
- 7. Fast and accurate short read alignment with Burrows-Wheeler transform
- 8. HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
- 9. Scale-Transferrable Object Detection
- 10. CNN 1.9 Pooling Layers