EE 6485 Computer Vision 計算機視覺

Fall 2022, Mon. 17:30 to 18:20 and Wed. 16:30 to 18:20, Location DELTA台達215
Instructor: Min Sun

TAs: FuEn Wang (王福恩) fulton84717@gmail.com
Hank Liao (廖宏儒) hankliao87@gmail.com
Justin Li (李明峯) li871030@gmail.com
Chia-Wei Wu (吳家維) zxc775206@gmail.com

Course Description

Can computers understand the visual world as we could? This course treats vision as a process of inference from noisy and uncertain data and emphasizes probabilistic, statistical, data-driven approaches. Topics include image processing; segmentation, grouping, and boundary detection; recognition and detection; motion estimation and structure from motion. This class will also lead you to the discussion of applications applying state-of-the-art techbiques in recognition, detection, and video analysis.

The course will consist of four programming projects and one final gruop project (max 5 members each team). Please find information about final project in the syllabus.

Prerequisites

This course requires programming experience (mainly Python) as well as linear algebra, basic calculus, and basic probability. Previous knowledge of visual computing will be helpful.

Textbook

Readings will be assigned in "Computer Vision: Algorithms and Applications" by Richard Szeliski. The book is available for free online or available for purchase.

Resource

Awesome computer vision github link
Awesome deep learning github link

Grading

Your final grade will be made up from

• 60% 4 programming projects
• 35% final projects (includes proposal, project pitch, midtern report, final project presentation, and final project report). 5 member each group maximum (Project Ideas)
• 5% class participation

You will lose 10% each day for late projects. However, you have three "late days" for the whole course. That is to say, the first 24 hours after the due date and time counts as 1 day, up to 48 hours is two and 72 for the third late day. This will not be reflected in the initial grade reports for your assignment, but they will be factored in and distributed at the end of the semester so that you get the most points possible.

Important Links

• Homeworks in elearn website

Contact Info and Office Hours

You can contact the professor with any of the following:

• Min Sun: sunmin@ee.nthu.edu.tw

Office Hours

• Min Sun, Delta 962, Tel: 035731058, Time: Mon. 15:30 - 16:30 & Tue. 16:00 - 17:00
• TAs, EECS Building 711, Time: Wed. 20:00 - 21:00

Tentative Syllabus

Week	Class Dates	Topic	Slides	Recording	Extra Info (e.g., Homework/Exam)
1	M, Sept. 12	Intro. to computer vision (CV)	pdf	link	Policy form out
	W, Sept. 14	Camera Model, Light and color	pdf	link	Policy form due link Team form is out.
		Python tutorial	slides		Homework 1 (hybrid image) out link
2	M, Sep. 19	Image filtering	pdf	link
	W, Sep. 21	Camera Geometry and calibration	pdf1,pdf2	link,link
3	M, Sep. 26	Single-view geometry	pdf	link	Final day to submit your team! intro
	W, Sep. 28	Holiday (Teachers' Day)			Homework 1 due Homework 2 out
4	M, Oct. 3	Epioplar geometry	pdf	link
	W, Oct. 5	Stereo system	pdf	link
5	M, Oct. 10	Holiday (National Day)
	W, Oct. 12	Multi-view geometry	pdf1 pdf2	link,link,link	Project proposal due
6	M, Oct. 17	Active strereo	pdf	link
	W, Oct. 19	Fitting and matching	pdf	link,link	Homework 2 due
7	M, Oct. 24	Colab and pytorch tutorial	link		ECCV-Trip
	W, Oct. 26	Project pitch (3-5 minutes, 20 team, 100 minutes)			ECCV-Trip
8	M, Oct. 31	Intro. to machine learning	pdf	link
	W, Nov. 2	Intro. to CNN-1	pdf	link,link	Homework 3 out
9	M, Nov. 7	Intro. to CNN-2	pdf	link
	W, Nov. 9	Training NN	pdf	link
10	M, Nov. 14	Object Detection and Beyond	pdf	link
	W, Nov. 16	Holiday (Sports Day)			Homework 3 due Homework 4 out
11	M, Nov. 21	Handle domain shift	pdf	link	Pre-recorded Lecture
	W, Nov. 23	Scaling-up Depth Estimation & Feature Tracking	pdf	link,link	Midterm project report due
12	M, Nov. 28	Scaling-up Flow	pdf	link
	W, Nov. 30	Neural Radiance Field (NeRF)	pdf1, pdf2	link,link
13	M, Dec. 5	Vision and language	pdf	link
	W, Dec. 7	Guest Lecture of Video Anomaly Detection & Transformer in CV.	pdf	link,link	Homework 4 due.
14	M, Dec. 12	Transformer in CV	pdf	link,link
	W, Dec. 14	How to keep up with the advance in CV?	pdf	link,link	YOLO-v7 guest lecture at 1:30 pm. Location: Delta 919.
15	M, Dec. 19	Final presentation
	W, Dec. 21	Final presentation
16	M, Dec. 26	Final presentation
	W, Dec. 28	Final presentation
17	M, Jan. 2	Holiday (New Year Day)
	W, Jan. 4	Buffer
18	M, Jan. 9	Final exam week
	W, Jan. 11	Final exam week			Final project report due

Project Proposal Format:

- Max 4 pages;
- 3 sections:

• Title and authors
• Sec 1. Intro: problem you want to solve and why
• Sec 2. Technical part: how do you propose to solve it?
• Sec 3. Milestones (dates and sub-goals)
• References

- Final format: pdf, please!

Project Progress (mid-term) Report Format:

- Max 4 pages;
- 3 sections:

• Title and authors
• Sec 1. Intro: problem you want to solve and why
• Sec 2. Technical part: how do you propose to solve it?
• Sec 3. Milestones achieved so far
• Sec 4. Remaining milestones (dates and sub-goals)
• References

- CVPR final format: pdf, please!

Project Final Report Format:

- Max 10 pages;
- Title and authors
- Abstract: short summary of the project with main results
- 6 sections:

• Sec 1. Introduction: introduce the problem you want to solve, expain why it is important to solve it; and indicate the method you used to solve it. add a concept figure showing the overall idea behind the method you are presenting.
• Sec 2.1. Review of previous work (i.e. previous methods that have explored a similar problem)
• Sec 2.2. Say why your method is better than previous work; and/or summarize the key main contributions of your work;
• Sec 3.1: Technical part: Summary of the technical solution
• Sec 3.2: Technical part: Details of the technical solution; you may want to decompose this section into several subsections; add figures to help your explanation.
• Sec 4: Experiments: present here experimental results of the method you have implemented with plots, graphs, images and visualizations.
• Sec 5: Conclusions: what's the take home message?
• Sec 6: References

- CVPR final format: pdf, please!
You can look at one of the recent publications (such as this) as an example.

Project Report Evaluation:

- Your project report will be evaluated based on the quality of the writing, the clarity of your technical explanation and, overall, how well you get your message across. If you follow the structure above, you'll have good chances to do a good job. :)

Project Source Code:

There is no need to attach a print out of the source codes to the manuscript. Final source codes of your working program need to be shared with TA and the instructor through elearn; this file is due on the project submission due date.

Project Pitch in Class:

- The presentation must be at most 5 minutes long. Please impress your audience with imaginary results to illustrate your idea.

Project Presentation in Class:

- The presentation must be at most 15 minutes long. Please see below for detailed presentation guidelines.

Presentation Format:

Your slides should consist of a title slide, followed by slides that discuss the following aspects of your project:

• Problem Motivation/Description
• Technical Approach
• Some Results

Evaluation:

- Your team will be evaluated based on the clarity of the presentation, quality of the slides, how well you get your message across, and how well you handle the questions at the end. Note that the presentation can still contain ongoing/preliminary results; final results may be included in the final report.
- We will use a peer-review system.

Acknowledgements:

The materials from this class rely significantly on slides prepared by other instructors, especially James Hayes, Fei-Fei Li, and Silvio Savarese. Each slide set and assignment contains acknowledgements.