EN.601.783 Vision as Bayesian Inference

EN.601.783

Vision as Bayesian Inference

Tues/Thurs: 9:00-10:15 Spring 2020, Olin 305.

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.

Reading Material

Course Notes and Handouts.
Papers and Journal Articles.
Instructor: Prof. Alan Yuille, ayuille1@jhu.edu, office 324b Clark Hall.
TA Office Hours: Wednesday 12:30 pm - 1:30 pm, Malone 222

Homework and Grading Plan

Homework assignments will have equal weights
25%-per-date deduction is applied after the due date
No assignments will be accepted after the third day.

Homework 1
Homework 2
Homework 3
Homework 4

Tentative Schedule

Lecture	Topics	Handouts	Supplements	Additional Readings
1	Introduction	Lecture1
2	Image Representation and PCA Sparsity	Lecture2	Background Material	Sparse Coding Eigenfaces
3	Dictionaries, Mixtures of Gaussians and Mini‐Epitomes	Lecture3	Math Notes	K-means++ Mini-Epitomes
4	Super Pixels and EM	Lecture4: EM and Affinity Lecture4: Super Pixels		ProtoObjects SLIC
5	Image Statistics and Weak Membrane Models	Lecture5 Part1 Lecture5 Part2		NonLinearTotalVariation StatisticsImagePatches
6	Edge Detection and Simple Semantic Segmentation	Lecture6 Part1 Lecture6 Part2	Math Notes Linear Filtering	EdgeDetection SemanticSegmentation
7	Decision Theory	Lecture7 Part1 Lecture7 Part2 Lecture7 Part3
8	Deep Networks and Edge Detection	Lecture8 Part1 Lecture8 Part2 Lecture8 Part3	Math Details	Holistically-Nested Edge Detection
9	MRF-MFT and Semantic Segmentation	Lecture9 Part1 Lecture9 Part2 Lecture9 Part3		DeepLab Fully Connected VRF
10	Weak Membrane, MRF and Annealing	Lecture10		Image Segmentation Belief Propagation and MFT
11	GrabCut and Belief Propagation	Lecture11 Part1 Lecture11 Part2 Lecture11 Part3		GrabCut CPMC
12	Probabilities on Graphs	Lecture12	Bayesian Inference	Neural Implementation
13	Stereo and Boltzmann Machine	Lecture13: DP and Stereo Lecture13: Boltzmann Machine		Bayesian Stereo Occlusions and Binocular Stereo Stereo_BP Stereo_CNN
14	Learning Exponential Models	Lecture14: Learning Exponential Models Lecture14: EM		Inducing Features of Random Fields FRAME
15	Hidden Markov Models	Lecture15		Extract Highlights
16	Motion	Lecture16	Math Details	Unsupervised BlackAnandanOpticalFlow HornShunck80 RobustPointMatching Motion coherence
17	Geometry and Motion	Lecture17 Part1 Lecture17 Part2		Manhattan World Two Algorithms Factorization methods Symmetry
18	Lighting	Lecture 18: LambertianLighting Lecture 18: Basri1 Lecture 18: Basri2		Lambertian Reflectance and Linear Subspaces GBR J53YuilleSnowEpsteinBelhumeur99
19	Adaboost	Lecture 19 Part1 Lecture 19 Part2	Notes	ChenYuille ViolaJonesAdaBoost
20	Deformable Parts Models and SVM	Lecture 20: Deformable Part Models Lecture 20: Support Vector Machines		strang_nonlinear_optimization YuilleHe2013 Latent Support Vector Example
21	Compositional Models	Lecture 21		JMIVyuilleB PGMM RCM10cvprLeoZhu
22	Deep Networks Attacks and Understanding	Lecture 22: Adversarial Defense Lecture 22: Understanding Part1 Lecture 22: Understanding Part2
23	Detecting and Parsing Humans; Compositional CNN	Lecture23: ParsingHumans Lecture23: Compositional Convolutional Neural Networks		XianjieChenHumanParsing2014 AdamKortylewskiComPnet2020
24	Computer Graphics and Computer Vision	Lecture 24 Part1 Lecture 24 Part2 Lecture 24 Part3
25	Beyond Standard Performance Evaluation	Lecture 25 Part1 Lecture 25 Part2 Lecture 25 Part3