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Adding Unlabeled Samples to Categories by Learned Attributes. Jonghyun Choi Mohammad Rastegari Ali Farhadi Larry S. Davis PPT Modified By Elliot Crowley. Constructing Training Set is Expensive. Human labeling is expensive Hard to find training samples for some categories - PowerPoint PPT Presentation
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Adding Unlabeled Samples to Categories by Learned Attributes
Jonghyun ChoiMohammad Rastegari
Ali FarhadiLarry S. Davis
PPT Modified By Elliot Crowley
Constructing Training Set is Expensive
• Human labeling is expensive• Hard to find training samples for some
categories– Heavy-tailed distribution of training set
• Solutions– Semi-supervised learning– Active learning
Assume that unlabeled data are drawn from the same
distribution of labeled data
Require humans in the loop
Adding Unlabeled Data Without .modeling a distributionhumans in the Loop
Number of training samplesIn SUN 09 Dataset
Salakhutdinov, Torralba, Tenenbaum (CVPR’11)
Long Tail Problem
• Training sets can have lots of objects in one pose
• f
• But not another.
Expanding Training Set by Attribute• Given small number of labeled training samples• Expanding visual boundary of a category to build a
better visual classifier
Given initial training set
Large UnlabeledImage Pool
Find bylow-level feature
Find byattributes
Similar to specific sample images
Dotted
whitishColor
Animal-like shape
Added back
From web
Why Attribute?• Low-level features are– Specific to shape, color, pose of given seeds– Expanding the visual boundary to the known region
• Add similar examples to the current set
• Attributes are– More general mid-level description than low-level
description (e.g., dotted)– Able to find visually unseen examples but
maintaining traits of the current set
Why Attributes Again?
• Not necessarily needed.
• Training set should have at least one example of each pose.
• Remainder are found by Exemplar method. (The strong point of this paper)
How to Add Samples by Attributes?
• Given candidate attributes– Pre-learned by an auxiliary data
• By learning– A discriminative set of attributes
• Find samples confident in combinations of attributes
Formulation• A joint optimization for– Learning classifier in visual feature space (wca)– Learning classifier in attribute space (wcv)– With finding the samples (I)
• Non-convex– Mixed integer program: NP-complete problem– Solution: Block coordinate-descent
Learning a classifier on visual feature space
Learning a classifier on attribute spacewith a selection criterion
Mutual ExclusionNot convex
discrete continuous
Formulation Detail
Classifier on visual feature space
Classifier on attribute space
Mutual Exclusion
Max-margin Classifieron visual feature space
Max-margin Classifieron attribute spacew/ Top-Lambda Selector
Mutual Exclusion
AttributeMapper
How to Build the Attribute Mapper• Candidate attribute set• Labeling attribute by human is expensive• Automatic attribute space generation by [1]– Learn in offline with any labeled set available on
the web• Attributes essentially reduce
feature space into binarydecision boundaries.
[1] Mohammad Rastegari, Ali Farhadi, David Forsyth, “Attribute Discovery via Predictable Discriminative Binary Codes”, ECCV 2012
Overview Diagram
Initial Labeled-Samples
Build Attribute Space
Project
Find Useful Attributes
Unlabeled Samples
Project
Choose Confident Examples To Add
Auxiliary data
• Exemplar: specific traits of a sample
Two Types of Attributes• Categorical: common traits of a category
Selected by Categorical Attributes
Initial Labeled Training Examples
Selected by Exemplar Attributes
Dotted
Animal-like shape
…
Exemplar Attributes
• Exemplar-SVM [1]– Captures example-specific
information• But requires lots of
negative samples to build
• Our approach– Difference on retrieval set
obtained by leave-one-out classifier and full-set classifier
[1] Tomasz Malisiewicz, Abhinav Gupta, Alexei A. Efros, “Ensemble of Exemplar-SVMs for Object Detection and Beyond”, ICCV 2011
Use allExcept each
Scor
e hi
gh
Given Training Samples
Modify the top-lambda-selector term in the formulation
Experimental Results• Dataset– A subset of ImageNet (ILSVRC 2010)– Target set: 11 categories (initial training #: 10/cat.,
testing: 500/cat.)• Both distinctive and fine-grained categories
– 6 vegetables (mashed potato, orange, lemon, green onion, acorn, coffee bean), 5 dog breeds (Golden Retriever, Yorkshire Terrier, Greyhound, Dalmatian, Miniature Poodle)
– Unlabeled set: randomly chosen samples from entire 1,000 categories in ILSVRC
– Auxiliary set: exclusive categories from target set
Download available soon in http://umiacs.umd.edu/~jhchoi/addingbyattr
Comparison with Other Approaches
• Red: using low-level features only
• Blue: our approach– Categorical attribute
only (Cat.)– Exemplar+categorical
attributes (E+C) Decrease!
Increase
Purity of Added Sample• Purity: # added samples of same category
total # added samples
Not very pure but still improves mAP!
Saturation of Performance
• Increase in mAP for method quickly saturates based on initial training set size.
• Most useful for small training sets.
Exemplar Attributes• Compare our exemplar attribute learning with
exemplar-SVM with large negative set
Summary• A new way of expanding a training set by attributes– A joint optimization formulation– Solve by block-coordinate descent
• Using both categorical and exemplar attributes to find examples
• Future work– Constraining expansion path
• Attribute may mislead as we added more samples due to low purity
Additional Slides
Varying number of added samples and accuracy