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Deep learning Computer Vision Detection paper

Table of contents

  1. Deep learning Computer Vision Detection paper
  2. Detection
    1. 1. Rich feature hierarchies for accurate object detection and semantic segmentation
    2. 2. Fast R-CNN
    3. 3. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
    4. 4. You Only Look Once: Unified, Real-Time Object Detection
    5. 5. SSD: Single Shot MultiBox Detector
    6. 6. Training Region-based Object Detectors with Online Hard Example Mining
    7. 7. R-FCN: Object Detection via Region-based Fully Convolutional Networks
    8. 8. Feature Pyramid Networks for Object Detection
    9. 9. YOLO9000: Better, Faster, Stronger
    10. 10. Mask R-CNN
    11. 11. Focal Loss for Dense Object Detection
    12. 12. Cascade R-CNN: Delving into High Quality Object Detection
    13. 13. YOLOv3: An Incremental Improvement
    14. 14. FCOS: Fully Convolutional One-Stage Object Detection
    15. 15. Objects as Points
    16. 16. CenterNet: Keypoint Triplets for Object Detection
    17. 17. EfficientDet: Scalable and Efficient Object Detection
    18. 18. YOLOv4: Optimal Speed and Accuracy of Object Detection
    19. 19. MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
    20. 20. VarifocalNet: An IoU-Aware Dense Object Detector
    21. 21. Sparse R-CNN: End-to-End Object Detection With Learnable Proposals
    22. 22. End-to-End Object Detection With Fully Convolutional Network

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

Table of contents