OBJECT DETECTION WITH DISCRIMINATIVELY TRAINED PART BASED

MODELS

PRESENTED BY

Xiaolong Wang

DETECTION

CHALLENGE

• Deformation

Part of the Slides From Ross Girshick

CHALLENGE

• Viewpoint

CHALLENGE

• Variable structure

CHALLENGE

Images from Chaitanya Desai

• 2-layer Model

• Deformable

DEFORMABLE PART MODELS

Leo Zhu, CVPR 2010

HOG PYRAMID

Root Filter

Part Filters

FORMULATIONOne root (i=0) + n parts.

Model Parameters for HOG

HOG Features Model Parameters for Deformation

INFERENCE

MULTI-VIEWS

LATENT ORIENTATION

• No orientation in PAMI paper (DPM v3)

• Use latent orientation (DPM v4) Guess what is it?

right-facing horse

UNSUPERVISED ORIENTATION CLUSTERING

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.

HOW IMPORTANT IT IS

One view:42.1% 3-view: 47.3% 3*2-view: 56.8%

• For horse:

HOW IMPORTANT IT IS

• For all classes (DPM v4):

LEARNING

• Linear Formulation Putting all features in one vector Latent variable z represents part locations (and

component index for multi-views)

LATENT SVM

• Iterative Algorithm with 2 steps: Calculate the latent variables (fixed ) Optimize the model parameters (fixed z).

LATENT SVM

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

LATENT SVM

• Hard Negative Mining

Carl Vondrick HOGgles, ICCV 2013

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

LATENT 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 INITIALIZATION

• Step-1: Only Train Root Filter positive data (highest overlap) No hard negative mining

Car

3-STEP INITIALIZATION

• Step-2: Merg Components Setting root selection as latent variable

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.

POST PROCESSING

• Bounding Box Regression Linear regression for (x1,y1,x2,y2)

• Non-Maximum Suppression Pick up high score boxes

• Context

CONTEXT

Marr Prize 2009

Context SVM,CVPR2010

segDPM,CVPR2013

NUMBERS

VOC 2010: 29.6 and 32.2

VOC 2007: 33.7 and 35.4

VOC 2010: segDPM(with tons of things) 40.4

LARGE-SCALE DATASET

• ImageNet 2013

DPM v4 in cpp

SUMMARY

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

QUESTIONS