BMI / CS 771 Learning Based Methods for Computer Vision
Fall 2022, MW 2:30PM - 3:45PM, 2535 Engineering Hall
TA: Abrar Majeedi
Instructor: Yin Li
Course DescriptionThe course addresses the problems of representation and reasoning for large amounts of visual data, including images and videos, medical imaging data, and their associated tags or text descriptions. We will introduce deep learning in the context of computer vision, and cover topics on visual recognition using deep models, such as image classification, object detection, human pose estimation, action recognition, 3D understanding, and medical image analysis. The course emphasizes the design of vision and learning algorithms and models, as well as their practical implementations.
Discussion GroupWe will use Piazza. You are encouraged to post your questions on the discussion board so that others can learn from your questions.
PrerequisitesStudents are strongly encouraged to have knowledge of computer vision or machine learning (such as CS 540) or medical image analysis (such as BMI/CS 567). In addition, the following skills are necessary for this class:
- Programming: Students should have basic proficiency in programming (Python). Projects are to be completed and graded in Python. TA's will support questions about Python.
- Math: Linear algebra, vector calculus, and probability theory.
GradingYour final grade will be made up from
- 48% 4 homework assignments (mini-projects) that involve programming
- 40% 1 course project with several milestones
- 12% Single-page course write-up
These late days are intended to cover unexpected clustering of due dates, travel commitments, interviews, hackathons, etc. Don't ask for extensions to due dates because we are already giving you a pool of late days to manage yourself.
Homework AssignmentsThe course will consist of 4 homework assignments. All assignments except the first one are team based. Teams of 2-3 students are preferred. In your submission, please clearly identify the contribution of all the team members. Please note that members in the same group might not necessarily get the same grade.
Please post all of your questions on Piazza so that others may learn from your questions as well. Do not email the professor or TA directly with homework questions.
All homeworks are to be submitted by midnight on the due date. All files should be included in a zip file named hwX_yourNetID.zip (where X is the homework number) and uploaded to Canvas. Late submissions should be emailed to the TA (and carbon the instructor). Please attach the zip file in your email.
All starter code and assignments will be in Python with the use of various third party libraries. We will make an effort to support MacOS, Windows, and Linux. The course includes a quick Python tutorial (optional) and assumes you have enough familiarity with procedural and object-oriented programming languages to complete the projects.
ProjectsThe final project is research-oriented. It can be a pure computer vision project or an application of exisiting vision methods in the student's own research area. You are expected to implement one (or more) related research papers, or think of some interesting novel ideas and implement them using the techniques discussed in class. Students are encouraged to propose their own project topics. You should work on the project in groups of 2-3. In your submission, please clearly identify the contribution of both group members.
There will be four checkpoints for the final project: a project proposal, an intermediate milestone report, a final project report and a project presentation. The details are listed below.
- Project Proposal (5%): This will be a single-page document. You will explain what problem you are trying to solve, why you want to solve it, and what are the possible steps to the solution.
- Project Mid-Term Report (5%): This will be a single-page brief summary of current progress, including your current results, the difficulties that arise during the implementation, and how your proposal may have changed in light of current progress.
- Project Final Report (15%): The final report will be a four-page document. You will describe the motivation of the project, the previous literate, your method and the results. You can reuse the materials that are presented in your proposal / mid-term report. Please include your source code in the submission.
- Project Presentation (15%, in class): Each team will be allocated a 15-min slot in class. This slot includes a 12-min presentation and a 3-min QA session.
Course Write-upThe course write-up will be a document that captures your reflection of the course work, e.g., what you have learned, what are the most interesting findings in the course. The write-up must be completed individually. It can be submited as a PDF file or a link to a webpage.
Academic IntegrityThis course follows the University of Wisconsin-Madison Code of Academic Integrity. Unless specifically authorized by the instructor, all coursework is to be done by the student working alone. Violations of the rules will not be tolerated.
You are permitted and encouraged to discuss ideas with other students. However, you are expected to implement the core components of each assignment / project on your own / with your team. You should not view or edit anyone else's code. You should not post code to Piazza, except for starter code / helper code that isn't related to the core project.
Contact Info and Office HoursIf possible, please use Piazza to ask questions and seek clarifications before emailing the instructor or TA.
- Yin: yin[dot]li[at]wisc[dot]edu
- Abrar: majeedi[at]wisc[dot]edu
- Yin, 1pm - 3pm Thursday (in-person at MSC 6730 or over Zoom)
- Abrar, 11am - 1pm Thursday (in-person at MSC 6729 or over Zoom).
|Wed, Sep 7||Introduction to Visual Recognition||See Canvas||Sign up for Piazza|
|Mon, Sep 12||Data Driven Paradigm for Computer Vision||Paper 1, 2|
|Wed, Sep 14||Image Processing using Python (Tutorial led by TA)||Homework 1 out|
|Mon, Sep 19||Introduction to Neural Networks||Ch 6, Deep Learning|
|Wed, Sep 21||Convolutional Neural Networks: Theory||Ch 9, Deep Learning|
|Mon, Sep 26||Convolutional Neural Networks: Practice||Paper 1, 2, 3|
|Wed, Sep 28||PyTorch and Cloud Computing (Tutorial led by TA)||Homework 1 due|
|Mon, Oct 3||Recurrent Neural Networks and Transformers: Part I||Ch 10, Deep Learning|
|Wed, Oct 5||Recurrent Neural Networks and Transformers: Part II||Paper 1, 2|
|Mon, Oct 10||Advanced Training||Ch 8, Deep Learning|
|Wed, Oct 12||Image Classification and Adversarial Samples||Paper 1, 2||Project Proposal due|
|Mon, Oct 17||Object Detection & Instance Segmentation: Part I||Paper 1, 2||Homework 2 out|
|Wed, Oct 19||Object Detection & Instance Segmentation: Part II||Paper 1, 2|
|Mon, Oct 24||Semantic Segmentation and Dense Image Labeling||Paper 1, 2|
|Wed, Oct 26||Human Pose Estimation||Paper 1, 2|
|Mon, Oct 31||Beyond Classification: Vision & Language||Paper 1, 2, 3|
|Wed, Nov 2||Action Recognition and Video Understanding: Part I||Paper 1, 2||Homework 2 due|
|Mon, Nov 7||Action Recognition and Video Understanding: Part II||Paper 1, 2|
|Wed, Nov 9||3D Scene Understanding: Part I||Paper 1, 2, 3|
|Mon, Nov 14||3D Scene Understanding: Part II||Paper 1, 2, 3|
|Wed, Nov 16||Mid-term Project Presentation|
|Mon, Nov 21||Mid-term Project Presentation||Mid-term report due|
|Wed, Nov 23||No Class; Happy Thanksgiving!|
|Mon, Nov 28||Generating Images and Beyond||Paper 1, 2|
|Wed, Nov 30||Self-supervised Visual Representation Learning||Paper 1, 2||Homework 3 out|
|Mon, Dec 5||Medical Image Analysis: Part I||Paper|
|Wed, Dec 7||Medical Image Analysis: Part II||Paper|
|Mon, Dec 12||Project Presentations|
|Wed, Dec 14||Project Presentations and Course Wrap-up||Homework 3 due
Project report due
Course write-up due
|Final Exam Period - not used|