RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
該論文錄用於AAAI 2020，相關工作已開源，論文鏈接 & 代碼鏈接。作者團隊：中科院自動化所&地平線，第一作者王紹儒是地平線實習生，來自中科院自動化所的碩士生。 論文對當前目標檢測及實例分割算法的現狀進行了簡要的概述，並對各種方法的優劣進行了簡要的分析，據此提出了一套完整的框架，同時完成目標檢測與實例分割任務，並且兩個任務相互輔助，同時取得了性能的提升。 一、問題提出 目標檢測與實例分割是計算
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相關文章
- 1. Deep Snake for Real-Time Instance Segmentation
- 2. 【xNets】MatrixNets: A New Scale and Aspect Ratio Aware Architecture for Object Detection
- 3. D2Det: Towards High Quality Object Detection and Instance Segmentation
- 4. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 5. PointSetNet：Point-Set Anchors for Object Detection,Instance Segmentation and Pose Estimation
- 6. 【SegNet】 A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- 7. Region-Based Convolutional Networks for Accurate Object Detection and Segmentation
- 8. Deep Watershed Transform for Instance Segmentation
- 9. DL-semantic segmentation/Classification+Localization/Object Detection/Instance Segmentation
- 10. RCNN：Rich feature hierarchies for accurate object detection and semantic segmentation