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CAP6412 – Spring 2022

Advanced Computer Vision (3 Credit Hours)

Course Content

This is an Advanced Computer Vision course which will expose graduate students to the cutting-edge research in Computer Vision. We will discuss research papers employing transformer to solve different computer vision problems.

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. Most of work in computer vision employing deep learning have leveraged Convolutional Neural Networks  (CNNs). Methods employing CNN learn local filters for different layers of the network with limited receptive fields, which  are fixed once learned. Recently, transformers have been introduced which employ self-attention, which are non-local and cover global receptive field, and  adapt based on image content. Interesting methods have been proposed to solve challenging computer vision problems employing transformer. This course will focus on transformers.

Grading Policy

  • Reports (you have to do only  50% of the papers): 10%
  • Presentation(roughly two): 20%
  • Lead Discussion Session (roughly two) : 5%
  • Attendance: 5%
  • Projects: 50%

Late Policy

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

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 generate own ideas to solve the same problem.
  • To work on research project and write a research paper
  •  Review/rehearsal of power point presentation meeting:

  • For Monday presentation

    • Friday a week before the scheduled presentation during  1:00PM office hours
  • For Wednesday presentation

    • Monday the week of the scheduled presentation  during 2:00PM office hours

Important Dates:


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.