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

D ETECTION

C HALLENGE Deformation Part of the Slides From Ross Girshick

C HALLENGE Viewpoint

C HALLENGE Variable structure

C HALLENGE Images from Chaitanya Desai

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

HOG P YRAMID Root Filter Part Filters

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

I NFERENCE

M ULTI - VIEWS

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

U NSUPERVISED ORIENTATION CLUSTERING

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.

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

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

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

L ATENT SVM

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

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

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

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

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

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

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.

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

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

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

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

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

Q UESTIONS