CVPR2010: Semi-supervised Learning in Vision: Part 1: Introduction
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Transcript of CVPR2010: Semi-supervised Learning in Vision: Part 1: Introduction
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Professor Horst Cerjak, 19.12.20051
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CVPR 2010 Tutorial Semi-Supervised Learning in
Vision
Inst. for Computer Graphics and VisionGraz University of Technology
A, Saffari, Ch. Leistner, H. Bischof
http://www.icg.tugraz.at/Members/Saffari/ssl-cvpr2010
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Typical Vision Tasks/Trends
Internet: Image Search/Classification Categorization
Huge Amounts of data, Huge labeling effort
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Typical Vision Tasks/Trends
Internet: Various Video and Image Databases
Huge Amounts of data, Partially/weakly labeled data
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Object Categorization
Typical Vision Tasks/Trends
Huge Amounts of data, Huge labeling effort
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VisipediaPerona 2009
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Surveillance: On-line data/Detection-Tracking
Typical Vision Tasks/Trends
Huge Amounts of data, On-line processing, Scene adaptation
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Many other tasks:• Tracking: On-line adaptation to object• Interactive segmentation: Changing model on
the fly• Interactive labeling: Suggestions as you label• …..
Typical Vision Tasks/Trends
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Requirements
Huge datasets
Unreliable (partial) labels
Changing environme
nts
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Requirements on Learner
On-line learning
RobustnessSemi-
supervised Learning
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Theory/Methods Covered
1. Semi-Supervised Learning2. Self-Training3. Generative Models4. Margin Assumption5. Cluster and Manifold Assumption6. Multi-View Learning7. Large-Scale, Multi-Class SSL and Online Learning8. Transfer Learning, Domain Adaptation, and Weakly
Related Data9. Multiple-Instance Learning
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Applications Covered
1. Object Detection2. Categorization3. Tracking4. Activity Recognition5. Segmentation
Using:BoostingRandom Forests
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Learning Tasks
• Unsupervised learning– Density estimation, Clustering, Dimensionality reduction.
• Semi-Supervised Clustering– Clustering with pair-wise constraints: must-link, cannot-link.
• Semi-Supervised Classification and Regression
• Supervised Learning– Classification, Regression.
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How do we get labels?Labels
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Semi-Supervised LearningSSL is Supervised Learning...
Goal: Estimate P(y|x) from Labeled DataDl={ (xi,yi) }
But: Additional Source tells about P(x)(e.g., Unlabeled Data Du={xj})
The Interesting Case:
)()()|()|(
xPypyxpxyp =
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Supervised learning
+ -
+ -
Maximum margin
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Can Unlabeled Data Help?
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low densityaround
decisionboundary
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SSL is biologically plausible
• Co-training by infants [Bahrick et.al. 2002]• Human change model once they see unlabeled data
[Zahki et.al. 2007,Zhu et.al. 2007,Vandist et.al. 2007]• Humans do On-line SSL [Zhu ICML 2010]
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Why?
1. Unlabelled data is cheap/free
2. Labeled data is hard to get
• human annotation is boring• labels may require experts• labels may require special devices• your graduate student is on vacation
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Online Learning in Perspective
Memory
Com
puta
tion
0 ∞0
∞
Online
Batch/Off-line
Incremental
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Why on-line learning?
Too much training data to fit in memory– Internet!!!
Sample generation process– Tracking, Co-Training
Changing processes– Changing Environment
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Why on-line learning?
Specializing– Forget irrelevant information– Specialize to current scene
Interactive Applications– Data labeling– Classifier Training– Specializing (Human in the loop)– Interactive Training (Segmentation)
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Robustness in Learning
Noisy input data
Label noise• Semi-supervised learning• Weakly-labeled data• Co-training• Self-learning• On-line learning
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Amir SaffariTheory
Christian LeistnerAlgorithms/Applications