CAP6412 – Spring 2019

 
Advanced Computer Vision (3 Credit Hours)

Course Contents

This is an Advanced Computer Vision which will expose graduate students to the cutting-edge research. In each class we will discuss one recent research paper related to active areas of current research, in particular employing Deep Learning. Computer vision has been very active area of research for many decades and researchers have been working on solving important challenging problems. During the last few years, Deep Learning involving Artificial Neural Networks has been disruptive force in computer vision. Employing deep learning, tremendous progress has been made in a very short time in solving difficult problems and very impressive results have obtained in image and video classification, localization, semantic segmentation, etc. New techniques, datasets, hardware and software libraries are emerging almost every day. Deep Computer vision is impacting research in Robotics, Natural Language understanding, Computer Graphics, multi-modal analysis etc.

Textbook

There is no text book for this class. We will discuss recent research papers.

Recommended supplemental textbook:

• Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning.

Meeting Format

The class will meet twice a week for a 75 min lecture, taught by the instructor

Recommended online courses and useful links

• http://cs231n.stanford.edu/ CS231n: Convolutional Neural Networks for Visual Recognition
• http://web.stanford.edu/class/cs224n/ CS224n: Natural Language Processing with Deep Learning
• http://rll.berkeley.edu/deeprlcourse/ CS 294: Deep Reinforcement Learning
• http://distill.pub/ Very nice explanations of some DL concepts
• https://class.coursera.org/ml003/lecture/preview
• https://media.nips.cc/Conferences/2016/Slides/6203-Slides.pdf
• https://media.nips.cc/Conferences/2016/Slides/6198-Slides.pdf
• https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html
• https://github.com/adeshpande3?tab=repositories
• http://cachestocaches.com/2017/12/favorite-deep-learning-2017/

Grading Policy

• Reports: 20%
• Presentation: 10%
• Attendance and Discussion: 20%
• Projects/Programs: 50%

Late Policy

• 0 for late Reports 
• 20% off per day, up to 4 days, for Projects/Programs

Topics

• Generative Adversarial Networks (GAN)
• Deep Reinforcement Learning
• Semi and Unsupervised Learning
• Human Action and Activity Recognition
• Vision and Language

Student Learning Outcomes

After the completion of the course, the students should be able to:

• Read and understand a research paper.
• Write a comprehensive review of the paper.
• To identify strong and weak points of the papers.
• To come up with own ideas to solve the same problem, which may lead to their first research paper.
• To implement known method or work and successfully complete individual project.

Important Dates:

See http://calendar.ucf.edu/2019/spring/

Statement on Academic Integrity:

The UCF Golden Rule will be observed in the class. Plagiarism and Cheating of any kind on an examination, quiz, or assignment will result at least in an “F” for that assignment (and may, depending on the severity of the case, lead to an “F” for the entire course) and may be subject to appropriate referral to the Office of Student Conduct for further action. I will assume for this course that you will adhere to the academic creed of this University and will maintain the highest standards of academic integrity. In other words, don’t cheat by giving answers to others or taking them from anyone else. I will also adhere to the highest standards of academic integrity, so please do not ask me to change (or expect me to change) your grade illegitimately or to bend or break rules for one person that will not apply to everyone.