EN.601.783

Vision as Bayesian Inference

Tues/Thurs: 9:00-10:15 Spring 2024, Homewood Campus, Hackerman B17 

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. The course will also cover advanced topics including CNNs, Transformers, NERF, diffusion models, vision-language models and LLMs/ChatGPT. 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

• Instructor: Prof. Alan Yuille, ayuille1@jhu.edu.
• TAs: Jieneng Chen (jchen293@jhu.edu, OH: Wed 8-9am), Steven Kan (pkan2@jhu.edu, OH: Tue 12-1pm)
• CAs: Junfei Xiao (junfei.xiao@jhu.edu, OH: Wed 1-2pm), Jiahao Wang (jwang384@jhu.edu, OH: Thu 1:30-2:30pm) and Rong Yang (ryang42@jh.edu, OH: Mon 9-10:00am)
• Office hours (OH): Malone 216; Any change will be posted in the "Discussions" section. 
• We encourages students  to initiate a discussion related to course in Canvas or piazza.
• Grading Plan:
  • (tentative) 4 homework assignments (to be posted here) + bonus participation points. Late homework will not be accepted unless permission is obtained well in advance in documented extenuating circumstances. Please email Prof. Yuille (ayuille1@jhu.edu) for approval and cc TAs.
  • We encourage students to participate in class, by asking questions to the professor. TAs will periodically record the names of those who ask interesting questions and award them bonus points that contribute to their final grades (1 point per class session, with a maximum of 3 points). Students not asking questions will not experience any negative effects on their grades.

Homework

• Homework1 covers the first 3 weeks of the courses. It is released in the section "Assignments". Please submit directly in Canvas.

Reading Material

• Course Notes and Handouts.
• Papers and Journal Articles.

Tentative Schedule

• More will be updated. Please see the schedule in Spring23 for reference. We will add more advanced topics including CNNs, Transformers, NERF, diffusion models, vision-language models and LLMs/ChatGPT.