TensorMask: A Foundation for Dense Object Segmentation
一.摘要 1.基於滑動窗口的目標檢測生成稠密，規則網格的bounding-box是很火熱的 2.相比之下，現代的實例分割方法被後述的方法所主導，它首先檢測對象邊界框，然後裁剪和分割這些區域，如Mask R-CNN所推廣的那樣 3.object detection中有dense的方法（如yolo，ssd，retinanet），也有R-CNN這類two-stage的方法；然而在instance seg
相關文章
- 1. 論文閱讀：TensorMask: A Foundation for Dense Object Segmentation
- 2. 【ICCV2019】TensorMask：A Foundation for Dense Object Segmentation
- 3. 神經網絡 | TensorMask: A Foundation for Dense Object Segmentation（何凱明團隊新作）
- 4. RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
- 5. 《Dense Residual Network for Retinal Vessel Segmentation》
- 6. Summary - A Transductive Approach for Video Object Segmentation
- 7. 論文筆記_2010-BMVC-Joint optimization for object class segmentation and dense stereo reconstruction
- 8. Focal Loss for Dense Object Detection
- 9. RetinaNet：Focal Loss for Dense Object Detection
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相關文章
- 1. 論文閱讀：TensorMask: A Foundation for Dense Object Segmentation
- 2. 【ICCV2019】TensorMask：A Foundation for Dense Object Segmentation
- 3. 神經網絡 | TensorMask: A Foundation for Dense Object Segmentation（何凱明團隊新作）
- 4. RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation
- 5. 《Dense Residual Network for Retinal Vessel Segmentation》
- 6. Summary - A Transductive Approach for Video Object Segmentation
- 7. 論文筆記_2010-BMVC-Joint optimization for object class segmentation and dense stereo reconstruction
- 8. Focal Loss for Dense Object Detection
- 9. RetinaNet：Focal Loss for Dense Object Detection
- 10. RetinaNet--Focal Loss for Dense Object Detection