O BJECT D ETECTION WITH D ISCRIMINATIVELY T RAINED P ART B ASED M ODELS PRESENTED BY Xiaolong Wang.
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Transcript of O BJECT D ETECTION WITH D ISCRIMINATIVELY T RAINED P ART B ASED M ODELS PRESENTED BY Xiaolong Wang.
![Page 1: O BJECT D ETECTION WITH D ISCRIMINATIVELY T RAINED P ART B ASED M ODELS PRESENTED BY Xiaolong Wang.](https://reader035.fdocuments.net/reader035/viewer/2022070410/56649f055503460f94c1ac2a/html5/thumbnails/1.jpg)
OBJECT DETECTION WITH DISCRIMINATIVELY TRAINED PART BASED
MODELS
PRESENTED BY
Xiaolong Wang
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DETECTION
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CHALLENGE
• Deformation
Part of the Slides From Ross Girshick
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CHALLENGE
• Viewpoint
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CHALLENGE
• Variable structure
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CHALLENGE
Images from Chaitanya Desai
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• 2-layer Model
• Deformable
DEFORMABLE PART MODELS
Leo Zhu, CVPR 2010
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HOG PYRAMID
Root Filter
Part Filters
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FORMULATIONOne root (i=0) + n parts.
Model Parameters for HOG
HOG Features Model Parameters for Deformation
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INFERENCE
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MULTI-VIEWS
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LATENT ORIENTATION
• No orientation in PAMI paper (DPM v3)
• Use latent orientation (DPM v4) Guess what is it?
right-facing horse
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UNSUPERVISED ORIENTATION CLUSTERING
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LATENT ORIENTATION
• 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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HOW IMPORTANT IT IS
One view:42.1% 3-view: 47.3% 3*2-view: 56.8%
• For horse:
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HOW IMPORTANT IT IS
• For all classes (DPM v4):
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LEARNING
• Linear Formulation Putting all features in one vector Latent variable z represents part locations (and
component index for multi-views)
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LATENT SVM
• Iterative Algorithm with 2 steps: Calculate the latent variables (fixed ) Optimize the model parameters (fixed z).
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LATENT SVM
• Detection on Positive Samples Sliding window Overlap with root-node window > 0.7
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LATENT SVM
• Hard Negative Mining
Carl Vondrick HOGgles, ICCV 2013
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LATENT 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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LATENT 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 INITIALIZATION
• Step-1: Only Train Root Filter positive data (highest overlap) No hard negative mining
Car
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3-STEP INITIALIZATION
• Step-2: Merg Components Setting root selection as latent variable
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3-STEP INITIALIZATION
• 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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POST PROCESSING
• Bounding Box Regression Linear regression for (x1,y1,x2,y2)
• Non-Maximum Suppression Pick up high score boxes
• Context
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CONTEXT
Marr Prize 2009
Context SVM,CVPR2010
segDPM,CVPR2013
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NUMBERS
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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LARGE-SCALE DATASET
• ImageNet 2013
DPM v4 in cpp
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SUMMARY
• 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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QUESTIONS