Detection

1. Detection: 第 1页 | 共 25 页

2. Object Detection: Intuition Detection ≈ Localization + Classification 第 2页 | 共 25 页

3. Outline •R-CNN •SPP-Net •Fast R-CNN •Unified Approach 第 3页 | 共 25 页

5. R-CNN: Pipeline Overview Step1. Input an image Step2. Use selective search to obtain ~2k proposals Step3. Warp each proposal and apply CNN to extract its features Step4. Adopt class-specified SVM to score each proposal Step5. Rank the proposals and use NMS to get the bboxes. Step6. Use class-specified regressors to refine the bboxes’ positions.Ross Girshick et al. Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR14 第 5页 | 共 25 页

6. R-CNN: Performance in PASCAL VOC07 • AlexNet(T-Net): 58.5 mAP • VGG-Net(O-Net): 66.0 mAP 第 6页 | 共 25 页

7. R-CNN: Limitation • TOO SLOWWWW !!! (13s/image on a GPU or 53s/image on a CPU, and VGG-Net 7x slower) • Proposals need to be warped to a fixed size. 第 7页 | 共 25 页

9. SPP-Net: Motivation • Cropping may loss some information about the object • Warpping may change the object’s appearance He et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, TPAMI15 第 9页 | 共 25 页

10. SPP-Net: Spatial Pyramid Pooling (SPP) Layer • FC layer need a fixed-length input while conv layer can be adapted to arbitrary input size. • Thus we need a bridge between the conv and FC layer. • Here comes the SPP layer. 第 10页 | 共 25 页

11. SPP-Net: Training for Detection(1) Conv5 feature map Conv5 feature map Conv5 feature map Image Pyramid FeatMap Pyramids conv Step1. Generate a image pyramid and exact the conv FeatMap of the whole image 第 11页 | 共 25 页

12. SPP-Net: Training for Detection(2) • Step 2, For each proposal, walking the image pyramid and find a project version that has a number of pixels closest to 224x224. (For scaling invariance in training.) • Step 3, find the corresponding FeatMap in Conv5 and use SPP layer to pool it to a fix size. • Step 4, While getting all the proposals’ feature, fine-tune the FC layer only. • Step 5, Train the class-specified SVM 第 12页 | 共 25 页

13. SPP-Net: Testing for Detection • Allmost the same as R-CNN, except Step3. 第 13页 | 共 25 页

14. SPP-Net: Performance • Speed: 64x faster than R-CNN using one scale, and 24x faster using five-scale paramid. • mAP: +1.2 mAP vs R-CNN 第 14页 | 共 25 页

15. SPP-Net: Limitation 2. Training is expensive in space and time. 1. Training is a multi-stage pipeline. FC layersConv layers SVM regressor store 第 15页 | 共 25 页

17. Fast R-CNN: Motivation Ross Girshick, Fast R-CNN, Arxiv tech report JOINT TRAINING!! 第 17页 | 共 25 页

18. Fast R-CNN: Joint Training Framework Joint the feature extractor, classifier, regressor together in a unified framework 第 18页 | 共 25 页

19. Fast R-CNN: RoI pooling layer ≈ one scale SPP layer 第 19页 | 共 25 页

20. Fast R-CNN: Regression Loss A smooth L1 loss which is less sensitive to outliers than L2 loss 第 20页 | 共 25 页

21. Fast R-CNN: Scale Invariance image pyramids （ multi scale ）brute force （ single scale ） Conv5 feature map conv • In practice, single scale is good enough. (The main reason why it can faster x10 than SPP-Net) 第 21页 | 共 25 页

22. Fast R-CNN: Other tricks • SVD on FC layers: 30% speed up at testing time with a little performance drop. • Which layers to fine-tune? Fix the shallow conv layers can reduce the training time with a little performance drop. • Data augment: use VOC12 as the additional trainset can boost mAP by ~3% 第 22页 | 共 25 页

23. Fast R-CNN: Performance • Without data augment, the mAP just +0.9 on VOC077 • But training and testing time has been greatly speed up. (training 9x, testing 213x vs R-CNN) • Without data augment, the mAP +2.3 on VOC127 第 23页 | 共 25 页

24. Fast-RCNN: Discussion about #proposal Are more proposals always better ？ NO! 第 24页 | 共 25 页

26. Unified Approach: Motivation No Need For Regions 第 26页 | 共 25 页

27. Unified Approach: Framework 第 27页 | 共 25 页 • Move Away from classification network • Use a deep network like GoogleNet • Divide the image into 7 by 7 grids • Each grid responsible for predicting the object center falling in the grid • Predict the class probabilities and coordinates for the object • Testing time reduces significantly as no regions are required • Loss function is combination of class probabilities error and bounding box regression error as in Fast RCNN

28. Unified Approach: Training 第 28页 | 共 25 页 • Most grid parameters will tend towards zero as one object will only contribute towards one grid • Introduce extra probability for background vs foreground • Probability error loss for a class activated only when foreground • Optimize for Pr(Class/ob) rather than Pr(Class) • Final probabilities calculated by Pr(ob)*Pr(Class/ob)

29. Unified Approach: Training 第 29页 | 共 25 页 • Run initial iterations by minimizing pr(ob) and pr(class/ob) separately • Can run joint minimization in later stages • The network predicts the bounding box taking convolutions from the whole image • This instigates error in localization • Penalize predictions which outputs lower iou by rescaling probabilities to the iou instead of 1

30. Unified Approach: Detection layer 第 30页 | 共 25 页

33. Unified Approach: Network 第 33页 | 共 25 页 Variant of GoogleNet with pooling layers replaced by convolutional layers which helps in localizing objects Leaky Relu layer with 1.1(X>0) + 0.1(x<0) increases map Logistic layer at the end to enforce predictions within 0 to 1

34. Unified Approach: Saliency 第 34页 | 共 25 页 • Predicts images at 45 fps • Competitive Performance with Fast Rcnn using Caffe net (MAP Score = 58.8) in VOC 2007 • Almost 95 times faster than Fast RCNN More details to be published in upcoming paper

35. 第 35页 | 共 25 页 THANKS

Detection

Detection

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