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

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.