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CAP6412 – Spring 2021

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 related to Adversarial Machine Learning and Explainable AI.

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. However, Deep Learning systems are brittle: small perturbations can cause state-of-the-art DNNs to misclassify an image. And Deep Learning systems are Blackbox; their predictions are not explainable. We will learn about these two topics in this course.

Grading Policy

  • Pop Quizzes (Before and After each paper discussion) (through zoom): 30%
  • Reports: 10%
  • Presentation: 5%
  • Attendance and Discussion: 10%
  • Projects/Programs: 45%

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 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.
  •  Review/rehearsal of power point presentation:

  • For Monday presentation

    • Email presentation: End of Tuesday the week before the presentation
    • Presentations Review: Wed 4:15 PM the week before the presentation
    • Rehearsal: Thursday 2:00PM the week before presentation
  • For Wednesday presentation

    • Email presentation: End of Thursday the week before presentation
    • Presentations Review: Friday 1:00 PM the week before  presentation
    • Rehearsal: Monday the week of presentation at 2:00PM

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.