Computer Vision II

CSE 152B, Spring 2020

Instructor: Manmohan Chandraker
Email: mkchandraker [AT] eng [DOT] ucsd [DOT] edu

Lectures: WF 6:30-7:50pm on Zoom
Instructor office hours: Thu 2-3pm on Zoom

TA: Rui Zhu (rzhu@eng.ucsd.edu)
TA office hours: Tue 1-2pm on Zoom

Discussion section: Mon 5-5:50pm on Zoom

Class discussion and message board: Piazza

Overview

Computer vision has made tremendous progress in recent years and is increasingly becoming a part of our daily lives. It seeks to analyze images to draw meaningful conclusions, which often requires drawing upon prior knowledge. This makes machine learning a useful tool, where the advent of deep neural networks has led to significant gains over the past five years. There is widespread acceptance that computer vision will play a major role in enabling technologies of the future, such as self-driving cars or augmented reality. The goal of the class is to equip students with the knowledge and skills to pursue higher studies or industry careers in modern computer vision.

Prerequisites

A background in linear algebra and calculus is required. Programming experience in Python is required. Courses that cover these might be Math 20F, CSE 100 or Math 176, CSE 101 or Math 188. Prior knowledge of basics in computer vision is recommended, as covered by CSE 152. Students are encouraged to contact the instructor if unsure about meeting any criteria for enrollment.

Course Format and Requirements

The course will primarily involve lectures by the instructor. The goal of the course is to develop an understanding of the current state of computer vision and gain appreciation of its limits and potential. Students are encouraged to actively ask questions that the instructor may discuss.

Grades will be weighted as 40% for a final exam, 25% for a mid-term and 35% for assignments. There will be three homework assignments and a few ungraded quizzes.

Topics

The course will explore the fundamentals of diverse topics in computer vision and deep learning. We will do so by studying canonical vision tasks and machine learning tools:

Outline

Apr 01: Introduction Quiz 1 Apr 03: Overview Apr 08: Correspondence Apr 10: Metric learning Apr 15: Keypoint detection and matching Quiz 2 Apr 17: Optical flow Apr 22: Structure from Motion Homework 1 Apr 24: Advanced Structure from Motion Apr 29: Practical Structure from Motion Quiz 3 May 01: Face Recognition 1 May 06: Face Recognition 2 May 08: Midterm Homework 2 May 13: Faces and humans May 15: Human pose estimation May 20: Human pose estimation 2 Quiz 4 May 22: Semantic segmentation Homework 3 May 27: Semantic segmentation 2 May 29: Object detection Jun 03: Object detection 2 Quiz 5 Jun 05: Review Jun 10: Exam

Resources

Manmohan Chandraker
Last modified: Fri, Mar 08, 2019