The document discusses modern convolutional object detectors, covering various methods like R-CNN, Faster R-CNN, R-FCN, and YOLO, along with their architectures and historical context. It references significant prior knowledge and academic sources related to object detection and computational efficiency benchmarks. The content emphasizes accuracy, processing time, and memory usage associated with these object detection techniques.

1. Modern Convolutional
Object Detectors
Kwanghee Choi
Sogang Univ.

2. Contents
- Prior Knowledge
- Object Detection
- R-CNN
- History
- Meta-architectures
- Faster R-CNN
- R-FCN
- YOLO
- Object Detection Benchmarks
- Accuracy
- Time
- Memory

3. Prior Knowledge: Neural Network
Ref. Fei-Fei Li & Andrej Karpathy & Justin Johnson, CS231n: Convolutional Neural Networks for Visual Recognition, Stanford Univ.

4. Prior Knowledge: Convolution
Ref. Aaditya Prakash, One by One Convolution: counter-intuitively useful, iamaaditya.github.io/2016/03/one-by-one-convolution

5. Prior Knowledge: Convolutional Neural Network
Ref. Fei-Fei Li & Andrej Karpathy & Justin Johnson, CS231n: Convolutional Neural Networks for Visual Recognition, Stanford Univ.

6. Prior Knowledge: Classification vs. Regression
Ref. Cyrille Rossant, IPython Interactive Computing and Visualization Cookbook, Packt Publishing

7. Prior Knowledge: Computer Vision Tasks
Ref. Fei-Fei Li & Andrej Karpathy & Justin Johnson, CS231n: Convolutional Neural Networks for Visual Recognition, Stanford Univ.

8. Object Detection: Demo
Ref. Joseph Redmon, YOLO v2, https://www.youtube.com/watch?v=VOC3huqHrss

9. Object Detection: History
Ref. Sam Albanie, R-FCN: Region-based Fully Convolutional Networks, VGG Reading Group

10. R-CNN: Pipeline
Ref. Ross Girshick et al., Rich feature hierarchies for accurate object detection and semantic segmentation, arXiv:1311.2524v5 [cs.CV] 22 Oct 2014

11. R-CNN: Selective Search
Ref. J. R. R. Uijlings et al., Selective Search for Object Recognition, IJCV 2013

12. Object Detection: History
Ref. Sam Albanie, R-FCN: Region-based Fully Convolutional Networks, VGG Reading Group

13. Object Detection: Meta-architectures
Ref. Jonathan Huang et al., Speed/accuracy trade-offs for modern convolutional object detectors, arXiv:1611.10012v3 [cs.CV] 25 Apr 2017

14. Faster R-CNN: Architecture
Ref. Shaoqing Ren et al., Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497v3 [cs.CV] 6 Jan 2016

15. Faster R-CNN: Region Proposal Network
Ref. Shaoqing Ren et al., Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497v3 [cs.CV] 6 Jan 2016

16. Object Detection: Meta-architectures
Ref. Jonathan Huang et al., Speed/accuracy trade-offs for modern convolutional object detectors, arXiv:1611.10012v3 [cs.CV] 25 Apr 2017

17. R-FCN: Architecture
Ref. Jifeng Dai et al., R-FCN: Object Detection via Region-based Fully Convolutional Network, arXiv:1605.06409v2 [cs.CV] 21 Jun 2016

18. R-FCN: Position-Sensitive Score Maps
Ref. Jifeng Dai et al., R-FCN: Object Detection via Region-based Fully Convolutional Network, arXiv:1605.06409v2 [cs.CV] 21 Jun 2016

19. Object Detection: Meta-architectures
Ref. Jonathan Huang et al., Speed/accuracy trade-offs for modern convolutional object detectors, arXiv:1611.10012v3 [cs.CV] 25 Apr 2017

20. YOLO: Architecture
Ref. Joseph Redmond et al., You Only Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640v5 [cs.CV] 9 May 2016

21. YOLO: Regression Model
Ref. Joseph Redmond et al., You Only Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640v5 [cs.CV] 9 May 2016

22. Object Detection: Meta-architectures
Ref. Jonathan Huang et al., Speed/accuracy trade-offs for modern convolutional object detectors, arXiv:1611.10012v3 [cs.CV] 25 Apr 2017

23. Object Detection Benchmarks: Accuracy
Ref. Jonathan Huang et al., Speed/accuracy trade-offs for modern convolutional object detectors, arXiv:1611.10012v3 [cs.CV] 25 Apr 2017

24. Object Detection Benchmarks: GPU Time
Ref. Jonathan Huang et al., Speed/accuracy trade-offs for modern convolutional object detectors, arXiv:1611.10012v3 [cs.CV] 25 Apr 2017

25. Object Detection Benchmarks: Memory Usage
Ref. Jonathan Huang et al., Speed/accuracy trade-offs for modern convolutional object detectors, arXiv:1611.10012v3 [cs.CV] 25 Apr 2017

26. Object Detection Benchmarks: Accuracy vs. Time
Ref. Jonathan Huang et al., Speed/accuracy trade-offs for modern convolutional object detectors, arXiv:1611.10012v3 [cs.CV] 25 Apr 2017

27. Welcome to the world where computer understands images.