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MIT Learning and Inference in Vision Prof. Bill Freeman, MW 2:30 – 4:00 Room: Course web page:

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Reading class We’ll cover about 1 paper each class. Seminal or topical research papers in the intersection of machine learning and vision. One student will present each paper. Then we’ll discuss the paper as a class. One student will write a computer example illustrating the paper’s main idea.

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Learning and Inference “Learning”: learn the parameter values or structure of a probabilistic model. –Look at many examples of people walking, and build up probabilistic model relating video images to 3-d motions. “Inference”: infer hidden variables, given a observations. –Eg, given a particular video of someone walking, infer their motions in 3-d.

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Statistical dependencies between variables Learning and Inference y1y2 Observed variables x1x2Unobserved variables

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Statistical dependencies between variables Learning and Inference Observed variables Unobserved variables “Learning”: learn this model, and the form of the statistical dependencies.

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Statistical dependencies between variables Learning and Inference y1y2 Observed variables x1x2Unobserved variables “Learning”: learn this model, and the form of the statistical dependencies. “Inference”: given this model, and the observations, y1 & y2, infer x1 & x2, or their conditional distribution.

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Cartoon history of speech recognition research 1960’s, 1970’s, 1980’s: lots of different approaches; “hey, let’s try this”. 1980’s Hidden Markov Models (HMM), statistical approach took off. 1990’s and beyond: HMM’s now the dominant approach. “The person with the best training set wins”.

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Same story for document understanding The person with the best training set wins.

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Computer vision is ready to make that transition Machine learning approaches are becoming dominant. We get to make and watch the transition to principled, statistical approach happen. It’s not trivial: issues of representation, robustness, generalization, speed, …

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Categories of the papers 1.Learning image representations 2.Learning manifolds 3.Linear and bilinear models 4.Learning low-level vision 5.Graphical models, belief propagation 6.Particle filters and tracking 7.Face and object recognition 8.Learning models of object appearance

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1 Learning image representations Example training image From

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1 Learning image representations From:

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2 Learning manifolds From: Joshua B. Tenenbaum, Vin de Silva, John C. Langford

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2 Learning manifolds From:

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2 Learning manifolds From:

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3 Linear and bilinear models From:

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4 Learning low-level vision From Y. Weiss, Images, under different lighting reflectanceillumination

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5 Graphical models, belief propagation From:

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6 Particle filters and tracking From:

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7 Face and object recognition From Viola and Jones,

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7 Face and object recognition From Viola and Jones,

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7 Face and object recognition From: Pinar Duygulu, Kobus Barnard, Nando deFreitas, and David Forsyth,

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8 Learning models of object appearance Weber, Welling, and Perona, Images containing the object Images not containing the object

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8 Learning models of object appearance Test images Weber, Welling, and Perona, Contains the object?

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8 Learning models of object appearance Weber, Welling, and Perona,

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Guest lecturers/discussants Andrew Blake (Condensation, Oxford/Microsoft) Baback Moghaddam (Bayesian face recognition, MERL) Paul Viola (Fast face recognition, MERL)

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Class requirements 1.Read each paper. Think about them. Discuss in class. 2.Present one paper to the class. 3.Present one computer example to the class. 4.Final project: write a conference paper related to vision and learning.

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1. Read the papers, discuss them Write down 3 insights about the paper that you might want to share with the class in discussion. Turn them in on a sheet of paper.

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2. Presentations about a paper About 15 minutes long. Set the stage for discussions. Review the paper. Summarize its contributions. Give relevant background. Discuss how it relates to other papers we’ve read. Meet with me two days before to go over your presentation about the paper.

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3. Programming example Present a computer implementation of a toy example that illustrates the main idea of the paper. Show trade-offs in parameter settings, or in training sets. Goal: help us build up intuition about these techniques. Ok to use on-line code. Then focus on creating informative toy training sets.

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Toy problems Simple summaries of the main idea. Identify an informative idea from the paper Make a simple example using it. Play with it.

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Toy problem by Ted Adelson

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Toy problem “If you can make a system to solve this, I’ll give you a PhD” by Ted Adelson

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Particle filter for inferring human motion in 3-d From: Hedvig Sidenbladh’s thesis,

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Particle filter toy example From: Hedvig Sidenbladh’s thesis,

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What we’ll have at the end of the class Non-negative matrix factorization example 1-d particle filtering example Boosting for face recognition Example of belief propagation for scene understanding. Manifold learning comparisons. … Code examples

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4. Final project: write a conference paper Submitting papers to conferences, you get just one shot, so it’s important to learn how to make good submissions. We’ll discuss many papers, and what’s good and bad about them, during the class. I’ll give a lecture on “how to write a good conference paper”. Subject of the paper can be: –A project from your own research. –A project you undertake for the class. Your idea One I suggest to you

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Feedback options At the end of the course: “it would have been better if we had done this…” –Somewhat helpful During the course: “I find this useful; I don’t find that useful…” –Very helpful

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What background do you need? Be able to read and understand the papers –Linear algebra –Familiarity with estimation theory –Image filtering Background in machine learning and computer vision.

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Auditing versus credit If you’re a student and want to take the class, sign up for credit. –You’ll stay more engaged. –Makes it more probable that I can offer the class again. But if you do audit: –Please don’t come to class if you haven’t read the paper. –I may ask you to present to the class, anyway.

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First paper Monday, Feb. 11. Emergence of simple-cell receptive field properties by learning a sparse code for natural images, Olshausen BA, Field DJ (1996) Nature, 381: Presenter: Bill Freeman Computational demonstration: need volunteer (software is available:

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Second paper Wednesday, Feb. 13. Learning the parts of objects by non-negative matrix factorization, D. D. Lee and H. S. Seung, Nature 401, (1999), and commentary by Mel. Presenter: need volunteer Computational demonstration: need volunteer

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