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Published byDominic Cobb Modified over 9 years ago
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O BJECT D ETECTION WITH D ISCRIMINATIVELY T RAINED P ART B ASED M ODELS PRESENTED BY Xiaolong Wang
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D ETECTION
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C HALLENGE Deformation Part of the Slides From Ross Girshick
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C HALLENGE Viewpoint
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C HALLENGE Variable structure
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C HALLENGE Images from Chaitanya Desai
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2-layer Model Deformable D EFORMABLE P ART M ODELS Leo Zhu, CVPR 2010
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HOG P YRAMID Root Filter Part Filters
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F ORMULATION One root (i=0) + n parts. Model Parameters for HOG HOG Features Model Parameters for Deformation
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I NFERENCE
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M ULTI - VIEWS
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L ATENT O RIENTATION No orientation in PAMI paper (DPM v3) Use latent orientation (DPM v4) Guess what is it? right-facing horse
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U NSUPERVISED ORIENTATION CLUSTERING
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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.
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H OW IMPORTANT IT IS One view:42.1% 3-view: 47.3% 3*2-view: 56.8% For horse:
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H OW IMPORTANT IT IS For all classes (DPM v4):
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L EARNING Linear Formulation Putting all features in one vector Latent variable z represents part locations (and component index for multi-views)
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L ATENT SVM
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Detection on Positive Samples Sliding window Overlap with root-node window > 0.7
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L ATENT SVM Hard Negative Mining Carl Vondrick HOGgles, ICCV 2013
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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
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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
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3- STEP I NITIALIZATION Step-1: Only Train Root Filter positive data (highest overlap) No hard negative mining Car
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3- STEP I NITIALIZATION Step-2: Merg Components Setting root selection as latent variable
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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.
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P OST P ROCESSING Bounding Box Regression Linear regression for (x1,y1,x2,y2) Non-Maximum Suppression Pick up high score boxes Context
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C ONTEXT Marr Prize 2009 Context SVM,CVPR2010 segDPM,CVPR2013
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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
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L ARGE - SCALE D ATASET ImageNet 2013 DPM v4 in cpp
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S UMMARY Although DPMs is loosing to CNNs, the techniques and small tricks we learned from DPMs help solving many other vision problems.
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Q UESTIONS
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