Table of contentsMIT 6.899 Learning and Inference in Vision
Reading class
Learning and Inference
Slide 4
Slide 5
Slide 6
Cartoon history of speech recognition research
Same story for document understanding
Computer vision is ready to make that transition
Categories of the papers
1 Learning image representations
Slide 12
2 Learning manifolds
Slide 14
Slide 15
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
Slide 21
Slide 22
8 Learning models of object appearance
Slide 24
8 Learning models of object appearance
Guest lecturers/discussants
Class requirements
1. Read the papers, discuss them
2. Presentations about a paper
3. Programming example
Toy problems
Toy problem
Slide 33
Particle filter for inferring human motion in 3-d
Particle filter toy example
What we’ll have at the end of the class
4. Final project: write a conference paper
Feedback options
What background do you need?
Auditing versus credit
First paper
Second paper
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