Advanced Techniques in
Visual Learning & Recognition

CMSC828I - Fall 2021 · Unive-remove-rsity of Maryland


A graduate-level course in computer vision, with an emphasis on high-level recognition tasks. We will read an eclectic mix of classic and contemporary papers on a wide-range of topics. The course structure will combine lectures, in-class discussions, assignments, and a course project. The goal of this course is to:

  1. Become knowledgeable about how a particular sub-topic evolved, the state-of-the-art, and the challenges that need to be addressed.
  2. Develop skills to critically read, analyze, and discuss a body of research.
  3. Learn to draw connections between different works and engage in productive discussions.

Student Masking

According to the University’s COVID-19 compliance guidelines, any student with an approved COVID-19 vaccination exemption must wear a mask at all times while indoors and outdoors when around others. These students must also be tested twice each week for COVID-19 and must sign the University Health Center’s Memorandum to Unvaccinated Individuals. Additionally, current County and University guidelines require all individuals to wear a mask indoors. Any student not abiding by these expectations may be in violation of the Code of Student Conduct, Part 10(e)(3): Failure to comply with a directive of University officials.


While there are no formal prerequisites for this course, familiarity with introductory courses in computer vision (CMSC426 or similar) and machine/deep learning learning (CMSC422 or similar) is assumed. If you have not taken courses covering this material, consult with the instructor. Note that a basic knowledge of linear algebra, probability, and calculus is required.


Where & when

CHE 2110
Tuesday, Thursday 3:30pm - 4:45pm

Final Exam

Tuesday, December 21 10:30am-12:30pm


Abhinav Shrivastava
4238 IRB
Office hours: TBD.

Teaching Assistant

Matthew Gwilliam
Office hours: TBD.

Hanyu Wang
Office hours: TBD.

Quick Links

Web Accessibility
CMSC828I Fall 2020
CMSC828I Fall 2019
CMSC828I Fall 2018


Date Topic Readings Slides Notes
I – Background and Foundations
Aug 31 Introduction to the class
Sep 2 Quiz

Sep 7
Sep 9
Introduction to Data slides Discuss add/drop with the instructor.
Sep 14 Data-driven methods in vision Additional Readings: slides
Sep 16
Sep 21
ConvNets and Architectures Important Architectures:
Architectures for Videos:
What goes on inside a CNN? Latest & greatest: Transformers
slides bold = required
II – Core Tasks
Sep 23
Sep 28
Sep 30
Two foundational tasks:
  • Object Detection
  • Image Segmentation
Background - Object Detection:
Background - Segmentation:
Sep 30
Oct 5
Two foundational tasks:
  • Object Detection
  • Image Segmentation
Single-stage object detection:
Semantic Segmentation:
Object Proposals:
slides Assignment 1 out
(Oct 5)
Oct 5
Oct 7
Oct 12
Two foundational tasks:
  • Object Detection
  • Image Segmentation
Multi-stage object detection:
Multi-stage detection & instance segmentation:
Transformers for detection/segmentation:
Analysis and diagnosis:
An awesome overview of recent detection methods -- MMDetection; arXiv 2019
slides Assignment 1 due
(Oct 12)
III – Additional Topics
Oct 12
Oct 14
Introduction to other tasks;
Human Pose Estimation
Background Reading:
Oct 19 Introduction to Reinforcement Learning
(Mara Levy)
Oct 21 - Mid-term Exam
IV – Guest Lectures
Oct 26 Self-supervised Learning
(Ishan Misra)
Oct 28 Learned Compression: Beyond Images and Video
(Saurabh Singh)
Nov 2 Neural Architecture Search
(Debadeepta Dey)
Nov 4 Vision Techniques for Reinforcement Learning
(Mary Levy)
Nov 9 Action Recognition Background:
Primary Readings:
Additional Readings:
Additional Readings (tasks and architectures):
Nov 18 Attributes Background:
Nov 23 Context Reasoning Primary Readings:
Background and Additional Readings:
Nov 23
Nov 30
Dec 2
All about 3D:
  • 3D Scene Understanding - Primitives and Reasoning
  • Objects + 3D
Required readings:
Additional and background readings: Great resource for 3D-related papers: 3D Machine Learning
Dec 2
Dec 7
Dec 9
Generative Models Primary Readings:
GANs: VAEs: Additional Readings (Assortment of Image Generative Models):
Dec 9 Misc. + Epilogue + Ethics Suggested Self-readings for Visual data mining and discovery: Additional readings: Readings on other topics, please refer to Fall 2019 website. slides
Final Exam: Dec 21 10:30am - 12:30pm


For a comprehensive review of Computer Vision, please refer to "Computer Vision: Algorithms and Applications by Richard Szeliski. The book is available for free online or available for purchase.

We will update this space with computer vision you can refer to.

Tutorials (libraries and computation resources)

Accommodations and Policies

Academic Integrity

Note that academic dishonesty includes not only cheating, fabrication, and plagiarism, but also includes helping other students commit acts of academic dishonesty by allowing them to obtain copies of your work. In short, all submitted work must be your own. Cases of academic dishonesty will be pursued to the fullest extent possible as stipulated by the Office of Student Conduct. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. For more information on the Code of Academic Integrity or the Student Honor Council, please visit

Excused Absence and Academic Accommodations

Any student who needs to be excused for an absence from a single lecture, recitation, or lab due to a medically necessitated absence shall:

  1. Make a reasonable attempt to inform the instructor of his/her illness prior to the class.
  2. Upon returning to the class, present their instructor with a self-signed note attesting to the date of their illness. Each note must contain an acknowledgment by the student that the information provided is true and correct. Providing false information to University officials is prohibited under Part 9(i) of the Code of Student Conduct (V-1.00(B) University of Maryland Code of Student Conduct) and may result in disciplinary action.
  3. This self-documentation may not be used for the Major Scheduled Grading Events as defined below.

Any student who needs to be excused for a Major Scheduled Grading Event, must provide written documentation of the illness from the Health Center or from an outside health care provider. This documentation must verify dates of treatment and indicate the time frame that the student was unable to meet academic responsibilities. No diagnostic information shall be given. The Major Scheduled Grading Events for this course include midterm and final exam. For class presentations, the instructor will help the student swap their presentation slot with other students.

It is also the student's responsibility to inform the instructor of any intended absences from exams and class presentations for religious observances in advance. Notice should be provided as soon as possible, but no later than the Monday prior to the the midterm exam, the class presentation date, and the final exam.

Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide a letter of accommodation from the Office of Disability Support Services within the first three weeks of the semester.

Other Accommodations and Policies

You can find the university’s course policies here.