Awesome-Computer Vision Detection paper

1. Rich feature hierarchies for accurate object detection and semantic segmentation, 2014, CVPR, Oral, Paper
2. Fast R-CNN, 2015, ICCV, Oral, Paper
3. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, 2015, NIPS, Paper
4. You Only Look Once: Unified, Real-Time Object Detection, 2016, CVPR, Oral, Paper
5. SSD: Single Shot MultiBox Detector, 2016, ECCV, Oral, Paper
6. Training Region-based Object Detectors with Online Hard Example Mining, 2016, CVPR, Oral, Paper
7. R-FCN: Object Detection via Region-based Fully Convolutional Networks, 2016, NIPS, Paper
8. Feature Pyramid Networks for Object Detection, 2017, CVPR, Paper
9. YOLO9000: Better, Faster, Stronger, 2017, CVPR, Oral, Paper
10. Mask R-CNN, 2017, ICCV, Oral, Paper
11. Focal Loss for Dense Object Detection, 2017, ICCV, Oral, Paper
12. Cascade R-CNN: Delving into High Quality Object Detection, 2018, CVPR, Spotlight, Paper
13. YOLOv3: An Incremental Improvement, 2018, Arxiv, Paper
14. FCOS: Fully Convolutional One-Stage Object Detection, 2019, ICCV, Paper
15. Objects as Points, 2019, Arxiv, Paper
16. CenterNet: Keypoint Triplets for Object Detection, 2019, ICCV, Paper
17. EfficientDet: Scalable and Efficient Object Detection, 2020, CVPR, Paper
18. YOLOv4: Optimal Speed and Accuracy of Object Detection, 2020, Arxiv, Paper
19. MobileDets: Searching for Object Detection Architectures for Mobile Accelerators, 2021, CVPR, Paper
20. VarifocalNet: An IoU-Aware Dense Object Detector, 2021, CVPR, Oral, Paper
21. Sparse R-CNN: End-to-End Object Detection With Learnable Proposals, 2021, CVPR, Paper
22. End-to-End Object Detection With Fully Convolutional Network, 2021, CVPR, Paper