BMI 826 / CS 838 Learning Based Methods in Computer Vision
Fall 2019, MW 2:30PM - 3:45PM, 3355 Engineering Hall
Instructor: Yin Li
TA: Zixuan Huang
Course Description
The course focuses on the problems of representation and reasoning for large amounts of visual data. These data include images and videos, medical imaging data and their associated tags or text. The majority of these problems stems from computer vision and machine learning. The content of the course is organized into two main sections. The first section introduces deep learning in the context of computer vision, including its theory, models, practice and systems. In the second part, we will cover topics on visual recognition, such as image classification, object detection, human pose estimation, action recognition, 3D understanding and medical image recognition.Discussion Group
We will use Piazza. Please post all of your questions on the discussion board so that others may learn from your questions as well. Do not email the professor or TA directly with homework questions.Prerequisites
Students are strongly encouraged to have knowledge of computer vision (CS 766) or medical image analysis (BMI/CS 767). No prior experience with machine learning is assumed, although previous knowledge of basic machine learning concepts will be helpful. 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.
Grading
Your final grade will be made up from- 45% 3 homework assignments (mini-projects) that involve programming
- 40% 1 course project with several milestones
- 5% Single-page course write-up
- 10% 2 in-class quizzes
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 Assignments
The course will consist of 3 homework assignments. The second and third assignments are team based. Teams of 2 students are preferred. In your submission, please clearly identify the contribution of all the team members. Please note that members in the same group will 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.
Projects
The final project is research-oriented. It can be a pure vision project or an application of vision techniques 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-up
The 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 Integrity
This 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. 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 Hours
If possible, please use Piazza to ask questions and seek clarifications before emailing the instructor or TA.- Yin: yin[dot]li[at]wisc[dot]edu
- Zixuan: zhuang356[at]wisc[dot]edu
- Yin, Tuesday 1-5pm, by appointment only (MSC 6730).
Syllabus
| Class Date | Topic | Slide | Reading | Assignment |
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| Wed, Sep 4 | Introduction to Visual Recognition | See Canvas | Sign up for Piazza | |
| Mon, Sep 9 | Theories of Visual Perception | |||
| Wed, Sep 11 | Image Processing using Python Tutorial led by TA | Homework 1 out | ||
| Mon, Sep 16 | Data Driven Paradigm | Paper 1, 2 | ||
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| Wed, Sep 18 | Introduction to Neural Networks | Ch 6, Deep Learning | ||
| Mon, Sep 23 | Convolutional Neural Networks: Theory | Ch 9, Deep Learning | ||
| Wed, Sep 25 | Convolutional Neural Networks: Practice | Paper 1, 2, 3 | ||
| Mon, Sep 30 | Recurrent Neural Networks | Ch 10, Deep Learning | Homework 1 due | |
| Wed, Oct 2 | Advanced Training | Ch 8, Deep Learning | ||
| Mon, Oct 7 | Deep Learning Systems (Tutorial) | Quiz 1 | ||
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| Wed, Oct 9 | Image Classification & Adversarial Samples | Paper 1, 2 | Project proposal due Homework 2 out |
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| Mon, Oct 14 | Object Detection & Instance Segmentation: Part I | Paper 1, 2, 3 | ||
| Wed, Oct 16 | Object Detection & Instance Segmentation: Part II | Paper 1, 2, 3 | ||
| Mon, Oct 21 | Semantic Segmentation | Paper 1 2 | ||
| Wed, Oct 23 | Human Pose Estimation | Paper 1, 2 | ||
| Mon, Oct 28 | Beyond Classification: Vision & Language | Paper 1, 2 | ||
| Wed, Oct 30 | Action Recognition | Paper 1, 2 | Homework 2 due | |
| Mon, Nov 4 | 3D Scene Understanding | Paper 1, 2 | ||
| Wed, Nov 6 | Deep Generative Models: Part I | Paper 1, 2 | Mid-term report due Homework 3 out |
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| Mon, Nov 11 | Deep Generative Models: Part II | Paper 1, 2 | ||
| Wed, Nov 13 | Medical Image Recognition | Paper | ||
| Mon, Nov 18 | Deep Learning for Medical Imaging (Guest lecture: Prof. Guanghong Chen) |
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| Wed, Nov 20 | Introduction to Deep Reinforcement Learning | Paper | ||
| Mon, Nov 25 | Self-supervised Visual Learning | Paper 1, 2 | Quiz 2 | |
| Wed, Nov 27 | No Class; Happy Thanksgiving! | |||
| Mon, Dec 2 | First Person Vision | |||
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| Wed, Dec 4 | Project Presentations | HW3 due at Dec 6th | ||
| Mon, Dec 9 | Project / Demo Presentations | |||
| Wed, Dec 11 | Project Presentations and Course Wrap-up | Project report due | ||
| Final Exam Period - not used | ||||