論文SDP + RCNN | Exploit All the Layers: Fast and Accurate CNN Object Detector with SDP and CRC
Abstract 本文提出了兩種目標檢測的措施，兼具精度與效率：1.scale-dependent pooling （精度）2. layer wise casaded rejection classifiers（效率） 1 Introduction 首先作者簡要介紹了RCNN等方法，FRCNN的缺點： 1. Fast-RCNN由於是從pooling層bounding box所以不能準確的識別小物體
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相關文章
- 1. 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. [論文解讀]Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty
- 4. 論文：Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo
- 5. 論文解讀：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 6. RCNN、SPPnet、Fast-RCNN 論文學習筆記
- 7. 《Receptive Field Block Net for Accurate and Fast Object Detection》論文筆記
- 8. 論文閱讀：Fast R-CNN
- 9. Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomo
- 10. 論文《Fast and Accurate Entity Recognition with Iterated Dilated Convolutions》