Teaching Assistants

Han Jian
Sima Chonghao
Tian Changyao

Course Coordinators

Chen Qingchen
Zhang Qifan


[May 30, 2022] Please send your final project report & code to your TA-in-charge by June 11. Details on here.
[May 13, 2022] The project presentation will be held on May 28th,2022 online.
[Mar 30, 2022] Assignment is now available. Demo code and demo dataset have been released. Final dataset and codebase will be released by Apr. 2
[Mar 17, 2022] Final Project is released. Proposal will be submitted on Mar 24th, 2022.
[Feb 28, 2022] Slides (PDF) are available in Lectures.
[Feb 25, 2022] Welcome to Advanced Computer Vision!

Course Description

This course involves computer vision, signal processing, deep learning and other fields of knowledge. It elaborates with the latest academic achievements and practical cases of industrial scenes and explain the classic and state-of-the-art methods in computer vision. By studying this course, students can learn basic theories and advanced methods in computer vision, and by understanding and exploring practical problems in the industry, enhance their cognitive ability and innovative ability to solve problems in computer vision.

Chapter 1 mainly introduces the concept of computer vision, image composition and basic image processing algorithms and introduces the neural network and deep learning framework.
Chapter 2 explains in detail the most cutting-edge research direction calculation method in the field of vision and the algorithm model optimization and performance improvement methods in visual scenes.
Chapter 3 covers the practical problems faced by computer vision and the solution ideas in combination with the specific scenes of industry.

As a computer vision course offered by university and industry, the course has four advantages:

Time and Venue

Spring Term (February - June, 2022)
  • Every Friday, 9:50am - 11:25am
  • Room 1102, No.3 Teaching Building
Office Hour:
  • Every Thursday, 7:00pm - 8:00pm
  • Tencent Meeting Room:595-8383-9016

Contact information

Dr. Li Yali: liyali13@mail.tsinghua.edu.cn
Dr. Dai Jifeng: daijifeng@sensetime.com
Dr. Li Hongyang: lihongyang@senseauto.com
Chen Qingchen: chenqingchen@sensetime.com


Grading Policy

1 Assignments: 30%
1 Final Project: 70%


Is this course hard for undergrad students?
The course is designed for senior undergrad and graduate students. We will show lots of interesting cases and hands-on experience about deep learning models. Some part of the lectures require calculus and linear algebra, but we will walk you through those knowledge. We think for undergrads, you will learn a lot at the end of the course through lectures, tutorials and the final project.

Can I work in groups for the Final Project?
Yes, in groups of up to three people. You can freely team up with classmates.

Is this course online for the whole semester?
Yes, this course is integrated online and offline teaching for the whole semester.Please contact TA to join the Rain Classroom if you need to participate in online coures.