Deep learning Computer Vision Detection paper

d9249·2022년 2월 18일
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Computer Vision

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Detection

1. Rich feature hierarchies for accurate object detection and semantic segmentation

2014, CVPR, Oral

2. Fast R-CNN

2015, ICCV, Oral

3. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

2015, NIPS

4. You Only Look Once: Unified, Real-Time Object Detection

2016, CVPR, Oral

5. SSD: Single Shot MultiBox Detector

2016, ECCV, Oral

6. Training Region-based Object Detectors with Online Hard Example Mining

2016, CVPR, Oral

7. R-FCN: Object Detection via Region-based Fully Convolutional Networks

2016, NIPS

8. Feature Pyramid Networks for Object Detection

2017, CVPR

9. YOLO9000: Better, Faster, Stronger

2017, CVPR, Oral

10. Mask R-CNN

2017, ICCV, Oral

11. Focal Loss for Dense Object Detection

2017, ICCV, Oral

12. Cascade R-CNN: Delving into High Quality Object Detection

2018, CVPR, Spotlight

13. YOLOv3: An Incremental Improvement

2018, Arxiv

14. FCOS: Fully Convolutional One-Stage Object Detection

2019, ICCV

15. Objects as Points

2019, Arxiv

16. CenterNet: Keypoint Triplets for Object Detection

2019, ICCV

17. EfficientDet: Scalable and Efficient Object Detection

2020, CVPR

18. YOLOv4: Optimal Speed and Accuracy of Object Detection

2020, Arxiv

19. MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

2021, CVPR

20. VarifocalNet: An IoU-Aware Dense Object Detector

2021, CVPR, Oral

21. Sparse R-CNN: End-to-End Object Detection With Learnable Proposals

2021, CVPR

22. End-to-End Object Detection With Fully Convolutional Network

2021, CVPR
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