Stat 232B-CS266B: 
 Statistical Computing and Inference
      in Vision and Cognition
                MW 3:30-4:50 pm, Winter 2019, Boelter Hall 5436

Course Description

This graduate level course introduces a broad range of advanced algorithms for statistical inference and learning on hierachical models. More specifically, this course will focus on grammatical models in the form of probabilistic And-Or Graphs, including i) Spatial AOG for object recognition and scene understanding; ii) Attribute-AOG for human pose and attribute inference; iii) Temporal-AOG for event understanding and behavior predictions; iv) Causal-AOG for causal-effects in human-object/scene interactions; and v) the joint STC-AOG for comprehensive scene and event parsing across multi-cameras. the lecture will covers topics on:

Prerequisites
Reference books
Instructors
Grading Plan: 4 units, letter grades
    The class will be divided into 8-10 teams for realizing various algorithms.
     TA will provide The grade will be based on four parts:
Class attendance and discussions 10%
First report on project topic: algorithm, case study, experiment design: due on the 2nd week 10%
Presentation ppt on the algorithm: 25 minutes, to be scheduled 20%
Code delivery to GitHub, and tested, by the final week 40%
Report in latex to add example to the draft, by the final week 20%
Tentative List of Topics    
[draft textbook, lecture notes, reading materals are distributed in CCLE] 
  Chapter 1   Introduction                                                             
   1. Overview of regimes of models from Stat232A
   2. Hierarchical STC-AOG representation and applications
   3. Project design and requirements
          
 Chapter 2  Spatial And-Or Graph            
   1. Terminology: basics of grammars, vocabulary, relations, parse graph, language   
   2. Characteristics of image grammars  
   3. And-Or graph for knowledge representation
   4. Some examples 

  Chapter 3  Learning And-Or Graph
   1. Parametric learning: EM algorithm, pursuit of contextural relations
   2. Structure learning: Block pursuit, AOG fragment pursuit
   3. Structure-parametric learning: Full-AOG and pruning

  Chapter 4  Inference and parsing algorithms 
   1. Traditional parse algorithms: CYK, Earley parser, chart parsing
   2. Inside-Outside: inferring and learning SCFG
   3. Alpha-beta-gamma scheduling
   4. Examples on object parsing             
Chapter 5 Attributed And-Or Graph 1. Attribute grammar 2. Example I: parsing man-made object and scenes 3. Example II: geometric attribute for scene parsing 4. Example III: appearance attributes for human parsing Chapter 6 Temporal And-Or Graph 1. Atomic actions 2. Representing events by T-AOG 3. Learning and pursuit T-AOG from videos and demonstrations 4. Event parsing and intent prediction with generalized Earley parser. Chapter 7 Fluent and Causal-And-Or Graph 1. Fluents of objects and scenes, and causal relations 2. Perceptual causality 3. Pursueing causal relations 4. Learning the causal-AOG: Pursuit and transfer Chapter 8 Joint parsing and integration 1. Scene centric parsing of object, scene and event 2. Examples: cross-view parsing of scenes and humans 3. Advanced topics: explanation and exploration with Logic + AOG + DNN.