Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
核心： 在Fast-RCNN的基礎上，設計了兩種改進方法： 一是尺度相關的池化SDP用於提升小尺寸物體檢測； 二是逐層級聯拒絕分類器CRC用於提速。 利用卷積網絡的每一層的特徵來拒絕負的Proposals； 對沒有被拒絕的proposals使用SDP進行評估； Scale-Dependent Pooling 該方法是基於Fast-RCNN的，SDP根據每個proposal的尺寸，從對應的卷積層特
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相關文章
- 1. 論文SDP + RCNN | Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
- 2. Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Casca
- 3. Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo
- 4. [論文解讀]Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty
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- 6. Semantic Flow for Fast and Accurate Scene Parsing
- 7. Fast and accurate short read alignment with Burrows-Wheeler transform
- 8. An Efficient and Accurate Scene Text Detector
- 9. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation
- 10. 顯著性檢測之Cascaded Partial Decoder for Fast and Accurate Salient Object Detection