1. O BJECT D ETECTION WITH D ISCRIMINATIVELY T RAINED P ART B ASED M ODELS PRESENTED BY Xiaolong Wang

2. D ETECTION

3. C HALLENGE Deformation Part of the Slides From Ross Girshick

4. C HALLENGE Viewpoint

5. C HALLENGE Variable structure

6. C HALLENGE Images from Chaitanya Desai

7. 2-layer Model Deformable D EFORMABLE P ART M ODELS Leo Zhu, CVPR 2010

8. HOG P YRAMID Root Filter Part Filters

9. F ORMULATION One root (i=0) + n parts. Model Parameters for HOG HOG Features Model Parameters for Deformation

10. I NFERENCE

11. M ULTI - VIEWS

12. L ATENT O RIENTATION No orientation in PAMI paper (DPM v3) Use latent orientation (DPM v4) Guess what is it? right-facing horse

13. U NSUPERVISED ORIENTATION CLUSTERING

14. L ATENT O RIENTATION Inference: Choose the best view and best orientation. Learning: Train the parameters for 3 views, and flip the weights to get 3*2 views.

15. H OW IMPORTANT IT IS One view:42.1% 3-view: 47.3% 3*2-view: 56.8% For horse:

16. H OW IMPORTANT IT IS For all classes (DPM v4):

17. L EARNING Linear Formulation  Putting all features in one vector  Latent variable z represents part locations (and component index for multi-views)

18. L ATENT SVM

19. Detection on Positive Samples  Sliding window  Overlap with root-node window > 0.7

20. L ATENT SVM Hard Negative Mining Carl Vondrick HOGgles, ICCV 2013

21. L ATENT SVM Hard Negative Mining  Small or no overlap  High detection score Maintaining Sample Cache  Select no more than 500 negative samples per image;  Cache size = 20000

22. L ATENT SVM Dual Method  Not scalable. Stochastic gradient descent(DPM v4)  Important: Shuffle everytime! LBFGS(DPM v5)  Second-order Newton Method  Faster & better performance

23. 3- STEP I NITIALIZATION Step-1: Only Train Root Filter  positive data (highest overlap)  No hard negative mining Car

24. 3- STEP I NITIALIZATION Step-2: Merg Components  Setting root selection as latent variable

25. 3- STEP I NITIALIZATION Step-3: Initialize Part Filters  Fix part number as 8 (DPM v4/5)  Sliding window, calculate L1/L2 norm of the positive weights.

26. P OST P ROCESSING Bounding Box Regression  Linear regression for (x1,y1,x2,y2) Non-Maximum Suppression  Pick up high score boxes Context

27. C ONTEXT Marr Prize 2009 Context SVM,CVPR2010 segDPM,CVPR2013

28. N UMBERS VOC 2010: 29.6 and 32.2 VOC 2007: 33.7 and 35.4 VOC 2010: segDPM(with tons of things) 40.4

29. L ARGE - SCALE D ATASET ImageNet 2013 DPM v4 in cpp

30. S UMMARY Although DPMs is loosing to CNNs, the techniques and small tricks we learned from DPMs help solving many other vision problems.

31. Q UESTIONS