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8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
http://slidepdf.com/reader/full/06-lfficcv2009-recognizing-multiple-objects-in-an-image-sharing-and-context 1/59
Multiclass object
detection
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Multiclass object detection
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Context: objects appear in configurations
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Generalization: objects share parts
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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How many categories?
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Slide by Aude Oliva
Muchas
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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How many object categories are there?
Biederman 1987
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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How many categories?
Probably this question is not even specificenough to have an answer
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Which level of categorization
is the right one?
Car is an object composed of:
a few doors, four wheels (not all visible at all times), a roof,
front lights, windshield
If you are thinking in buying a car, you might want to be a bit more specific about
your categorization level.
?
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Entry-level categories(Jolicoeur, Gluck, Kosslyn 1984)
Typical member of a basic-level category arecategorized at the expected level
Atypical members tend to be classified at a
subordinate level.
A birdAn ostrich
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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We do not need to recognize the exact category
A new class can borrow information from similar
categories
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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So, where is computer vision?
Well
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Multiclass object detectionthe not so early days
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Multiclass object detectionthe not so early days
Schneiderman-Kanade multiclass object detection
Using a set of independent binary classifiers was a common strategy:
Viola-Jones extension for dealing with rotations
- two cascades for each view
(a) One detector for each class
There is nothing wrong with this approach if you have access to
lots of training data and you do not care about efficiency.
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Generalizing Across Categories
Can we transfer knowledge from one object category to another? Slide by Erik Sudderth
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Shared features
Is learning the object class 1000 easier thanlearning the first?
Can we transfer knowledge from one object to
another?
Are the shared properties interesting bythemselves?
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Multitask learningR. Caruana. Multitask Learning. ML 1997
MTL improves generalization by leveraging the domain-specific information contained
in the training signals of related tasks. It does this by training tasks in parallel while using
a shared representation.
vs.
Sejnowski & Rosenberg 1986; Hinton 1986; Le Cun et al. 1989; Suddarth & Kergosien
1990; Pratt et al. 1991; Sharkey & Sharkey 1992;
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Multitask learning
horizontal location of doorknob
single or double door
horizontal location of doorway centerwidth of doorway
horizontal location of left door jamb
horizontal location of right door jamb
width of left door jamb
width of right door jambhorizontal location of left edge of door
horizontal location of right edge of door
Primary task: detect door knobs
Tasks used:
R. Caruana. Multitask Learning. ML 1997
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Sharing invariancesS. Thrun.Is Learning the n-th Thing Any Easier Than Learning The First? NIPS 1996
Knowledge is transferred between tasks via a learned model of the invariances
of the domain: object recognition is invariant to rotation, translation, scaling,
lighting, These invariances are common to all object recognition tasks.
Toy world
Without sharing
With sharing
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Convolutional Neural Network
Translation invariance is already built into the network
The output neurons share all the intermediate levels
Le Cun et al, 98
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Sharing transformations
Miller, E., Matsakis, N., and Viola, P. (2000). Learning from one example through
shared densities on transforms. In IEEE Computer Vision and Pattern Recognition.
Transformations are shared
and can be learnt from other tasks.
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Sharing in constellation models
Pictorial StructuresFi schler & Elschlager, IEEE Trans. Comp. 1973
Constellation ModelF ei-F ei , F ergus, Perona, ICCV 2003
SVM DetectorsHei sele, Poggi o, et. al., NIPS 2001
Model-Guided SegmentationMor i , Ren, Efros, & Mal i k, CVPR 2004
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Reusable Parts
Goal: Look for a vocabulary of edges that reduces the number of
features.
Krempp, Geman, & Amit Sequential Learning of Reusable Parts for Object Detection.
TR 2002
N u m b e r o f f e a t u
r e s
Number of classes
Examples of reused parts
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Specific feature
Non-shared feature: this feature
is too specific to faces.
pedestrian
chair
Traffic light
sign
face
Background class
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Shared feature
shared feature
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Additive models and boosting
Torralba, Murphy, Freeman. CVPR 2004. PAMI 2007
Screen detector
Car detector
Face detector
Binary classifiers that share features:
Screen detector
Car detector
Face detector
Independent binary classifiers:
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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50 training samples/class
29 object classes2000 entries in the dictionary
Results averaged on 20 runs
Error bars = 80% interval
Torralba, Murphy, Freeman. CVPR 2004. PAMI 2007
Shared features
Class-specific features
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Generalization as a function of object
similarities
12 viewpoints12 unrelated object classes
Number of training samples per class Number of training samples per class
A r e a u n d e r R O C
A r e a u n d e r R O C
K = 2.1 K = 4.8
Torralba, Murphy, Freeman. CVPR 2004. PAMI 2007
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Opelt, Pinz, Zisserman, CVPR 2006
Efficiency Generalization
J. Shotton, A. Blake, R. Cipolla.
Multi-Scale Categorical Object Recognition Using
Contour Fragments. In IEEETrans. on PAMI,
30(7):1270-1281, July 2008.
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Sharing patches
Bart and Ullman, 2004For a new class, use only features similar to features that where good for other
classes:
Proposed Dog
features
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Some more references
Baxter 1996
Caruana 1997
Schapire, Singer, 2000
Thrun, Pratt 1997
Krempp, Geman, Amit, 2002
E.L.Miller, Matsakis, Viola, 2000
Mahamud, Hebert, Lafferty, 2001
Fink et al. 2003, 2004 LeCun, Huang, Bottou, 2004
Holub, Welling, Perona, 2005
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Modeling object
relationships
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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The guess what I am trying to detect challenge
The detector challenge: by looking at the output of a detector on a random set
of images, can you guess which object is it trying to detect?
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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What object is detector trying to
detect?
The detector challenge: by looking at the output of a detector on a random set
of images, can you guess which object is it trying to detect?
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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1. chair, 2. table, 3. road, 4. road, 5. table, 6. car, 7. keyboard.
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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The context challenge
How far can you go without
using an object detector?
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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What are the hidden objects?
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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What are the hidden objects?
Chance ~ 1/30000
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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p(O | I) Ep(I|O) p(O)
Object model Context model
imageobjects
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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p(O | I) Ep(I|O) p(O)
Object model Context model
Full jointScene model Aprox. joint
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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p(O | I) Ep(I|O) p(O)
Object model Context model
Full jointScene model Approx. joint
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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p(O | I) Ep(I|O) p(O)
Object model Context model
Full jointScene model
p(O) = 74p(Oi|S=s) p(S=s)s i
Approx. joint
office street
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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p(O | I) Ep(I|O) p(O)
Object model Context model
Full jointScene model Approx. joint
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Pixel labeling using MRFs
Enforce consistency between neighboring labels,
and between labels and pixels
Carbonetto, de Freitas & Barnard, ECCV¶04
Oi
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Beyond nearest-neighbor grids
Most MRF/CRF models assume nearest-neighbor graph topology
This cannot capture long-distance
correlations
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Object-Object Relationships
Use latent variables to induce long distance correlationsbetween labels in a Conditional Random Field (CRF)
He, Zemel & Carreira-Perpinan (04)
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Object-Object Relationships
[KumarHebert 2005]
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Fink & Perona (NIPS 03)Use output of boosting from other objects at previous
iterations as input into boosting for this iteration
Object-Object Relationships
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Objects in Context
Building,
boat, motorbike
Building, boat, person
Water,
sky
Road
Most consistent labeling
according to object co-
occurrences& locallabel
probabilities.
Boat
Building
Water
Road
A. Rabinovich, A. Vedaldi, C. Galleguillos, E. Wiewiora
and S. Belongie. Objects in Context. ICCV 2007
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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52
Objects in Context:
Contextual Refinement
Contextual model based on co-occurrences
Try to find the most consistent labeling with
high posterior probability and high mean
pairwise interaction.
Use CRF for this purpose.Boat
Building
Water
Road
Independent
segment classificationMean interaction of all label pairs
(i,j) is basically the observed label co-
occurrences in training set.
Slide by GokberkCinbis
d ff l b
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Detecting difficult objects
Office Maybethere is
a mouse
Start recognizing the scene
Torralba, Murphy, Freeman. NIPS 2004.
i diffi l bj
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Detecting difficult objects
Detect first simple objects (reliable detectors) that provide strong
contextual constraints to the target (screen -> keyboard -> mouse)
Torralba, Murphy, Freeman. NIPS 2004.
D i diffi l bj
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Detecting difficult objects
Detect first simple objects (reliable detectors) that provide strong
contextual constraints to the target (screen -> keyboard -> mouse)
Torralba, Murphy, Freeman. NIPS 2004.
BRF f d i l
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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BRF for car detection: topology
Torralba Murphy Freeman (2004)
BRF f d i l
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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BRF for car detection: results
Torralba Murphy Freeman (2004)
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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A car out of context is less of a car
Car Building Road
b F G b F G b F G
From image
From detectors
Thresholded beliefs
C t t l bj t l ti hi
8/3/2019 06 - LFF_ICCV2009 Recognizing Multiple Objects in an Image - Sharing and Context
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Contextual object relationshipsCarbonetto, de Freitas & Barnard (2004) Kumar,Hebert (2005)
Torralba Murphy Freeman (2004)
Fink & Perona (2003)E. Sudderth et al (2005)