Vision as Bayesian Inference

Tues/Thurs: 9:00-10:15 Spring 2022

Course Description

This course models vision as Bayesian Inference. It concentrates on visual tasks such as segmenting images, detecting objects in images, and recognizing objects. Its goal is to describe the state of the art techniques. The handouts consist of copies of the lecture notes and related papers.

Important information

Reading Material

Tentative Schedule

Lecture Topics Handouts Supplements Additional Readings
1 (01/25/2022) Introduction
2 (01/27/2022)
Image Representation and PCA Sparsity Lecture2
Lecture2 Notes
Robust Face Recognition
Sparse Representation
3 (02/01/2022) Dictionaries, Mixtures of Gaussians, Miniā€Epitomes, EM Lecture3 (1)
Lecture3 (2)
4 (02/03/2021) Super Pixels Lecture4
Lecture4 Notes ProtoObjects
5 (02/08/2022) Image Statistics and Weak Membrane Models Lecture5 Lecture5 Notes
6 (02/10/2022)
Edge Detection and Simple Semantic Segmentation Lecture6 Part1
Lecture6 Part2
Lecture6 Part3
7 (02/15/2022) Decision Theory Lecture7 Part1
Lecture7 Part2
Lecture7 Part3
Lecture7 Part4
Lecture7 Notes

8 (02/17/2022) Deep Networks and Edge Detection Lecture8 Part1
Lecture8 Part2
Lecture8 Part3

Holistically-Nested Edge Detection
9 (02/22/2022) MRF-MFT and Semantic Segmentation Lecture9 Part1
Lecture9 Part2
Lecture9 Part3
Fully Connected CRF
10 (02/24/2022) Weak Membrane, MRF and Annealing Lecture10 Part1
Lecture10 Part2
Lecture10 Part3
Image Segmentation
Grab Cut
11 (03/01/2022) GrabCut and Belief Propagation Lecture11 Part1
Lecture11 Part2
Tutorial on Bayesian Inference
12 (03/03/2022) Probabilities on Graphs Lecture12 Part1
Lecture12 Part2
Occlusions and Binocular Stereo
Stereo BP
Stereo CNN
13 (03/08/2021) Stereo and Boltzmann Machine Lecture13 Part1
Lecture13 Part2
Random Field
Learning Exponential Models
14 (03/10/2022) Learning Exponential Models, Hidden Markov Models Lecture14: HMM Lecture14: BoltzmannMachine HMM Math Details
Chang Peng et al 2002
15 (03/15/2022) Lighting Lecture15 Part1
Lecture15 Part2
Lambertian Lighting
SVD and Integrability
Lambertian Reflectance
16 (03/15/2022) AdaBoost Lecture16 Part1
Lecture16 Part2
Lecture Notes
Math Details
Text Detection
Object Detection
17 (03/17/2022) Support Vector Machine Lecture17 Part1
Latent SVM
Strang Nonlinear Optimization
Yuille & He 2013
Spring Break on 03/21/2022 to 03/27/2022
18 (03/29/2022) Deformable Part Model Lecture18 Part1
Lecture18 Part2
19 (03/31/2022) Compositional Model Lecture 19
Paper 1
Paper 2
Paper 3
20 (04/05/2022) Parsing Lecture 20
CompNet Paper1 CompNet Paper2 Human Parsing
21 (04/07/2022) Compositional Generative Networks Lecture 21 Part 1 Lecture 21 Part 2 Semantic Parts
You Only Annotate Once
22 (04/12/2022) Deep Networks Attacks and Understanding Lecture 22 Paper1 Paper2
23 (04/14/2022) Computer Graphics and Computer Vision Lecture 23 SemanticPartsCGViewpoint
24 (04/19/2022) Model Robustness and Beyond Lecture 24 Part1 Lecture 24 Part2
Lecture 24 Part3
25 (04/21/2022) Analysis By Synthesis Lecture 25
Image Parsing 1
Image Parsing 2
Image Parsing 3
Region Competition
26 (04/26/2022) GAN Lecture 26 Part1
Lecture 26 Part2
Paper 1
Paper 2
Paper 3
Paper 4
Paper 5