Sp21 - ADV TOPICS IN COMP VISION-WB (17590)
Course title:
EE 381V: Advanced Topics in Computer Vision
Term:
Spring 2021
Meeting times and location:
MW 5:00-6:30pm (online)
Course Description and Prerequisites
This is a research-oriented advanced class that intends to focus on the latest frontier of computer vision. It describes computer vision algorithms that make sense of photographs, video, and other imagery. Applications include robotics, content creation, entertainment, medical image analysis, smart home, security, and HCI, among many others. Through this course, the students will digest and practice their knowledge and skills by many open discussions in classes, and will obtain in-depth experience with a particular research topic through a final project.
Students should have taken the following courses or equivalent: Convex Optimization (381K-18), and Probability & Stochastic Process I (381J).
Previous knowledge of the following courses is helpful, but not necessary: Digital Video (381K-16), Statistical Machine Learning (381V), Data Mining (381L-10), or Cross-Layer Machine Learning HW/SW Design (382V).
Coding experiences with Python are necessary and assumed. Previous knowledge of C/C++, MATLAB or Tensorflow is very helpful, but not necessary.
Instructor Information
Name:
Dr. Zhangyang (Atlas) Wang
Telephone number:
512-471-1866
Email address:
Office location:
EER 6.886
Zoom Link:
sent to registered students
Textbook and/or Resource Material
This course does not follow any textbook closely. Among many recommended readings are:
Grading Policies
Grading will be based on class participation (10%), one mid-term exam (15%), and one final project (75%) (milestone 1 progress report 15% + milestone 2 progress report 15% + presentation 20% + final report 15% + code review 10%). There will be no final exam.
Course Topics
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Pattern Recognition and Machine Learning, Christopher M. Bishop (2006).
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Computer Vision: Algorithms and Applications, Richard Szeliski (2010).
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Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016).
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Diving into Deep Learning, Aston Zhang, Zack Lipton, Mu Li and Alex Smola (2019).
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One project to receive the Best Project Award, voted by all class members. (+5%)
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Projects in the novel, interdisciplinary domains (some examples: 5G/6G telecommunication, brain-computer interface, economics & markets, COVID-19, etc.), judged by the instructor. (+2%)
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For late submission, each additional late day will incur a 10% penalty.
Introduction and Computer Vision Basics Slides
Deep Learning Basics (1): Building A Deep Network
Deep Learning Basics (2): Representative Models and Tasks
Deep Learning Basics (3): Advanced Models and Optimization Slides
Topic I: Label-Efficient Learning (1): Semi-Supervised Learning
Topic I: Label-Efficient Learning (2): Few-Shot & Active Learning
Topic I: Label-Efficient Learning (3): Transfer & Multi-Task & Self-Supervised Learning Slides
- Class Cancelled (winter storm) -
- Class Cancelled (winter storm) -
Topic II: Resource-Efficient Learning (1): Model Compression
Topic II: Resource-Efficient Learning (2): Efficient Training Slides
Topic III: Robust Vision (1): Image Enhancement
Topic III: Robust Vision (2): Uncertainty
Topic III: Robust Vision (3): Domain Generalization
Topic III: Robust Vision (4): Adversarial Robustness Slides
Topic IV: Generative Models - GANs and VAEs (1)
Topic IV: Generative Models - GANs and VAEs (2)
Topic IV: Generative Models - GANs and VAEs (3) Slides
Topic V: AutoML and Meta Learning (1)
Topic V: AutoML and Meta Learning (2)
Topic V: AutoML and Meta Learning (3) Slides
Topic VI: Vision and Language (1)
Topic VI: Vision and Language (2)
Topic VI: Vision and Language (3) Slides
Special Topics (1): Scaling up Vision with Synthetic Data Slides
Special Topics (2): Visual Transformers Slides
Special Topics (3): Ethics and Privacy in Computer Vision Slides
Class Project Presentation (1)
Class Project Presentation (2)
Acknowledgement
Many materials included in this course are adapted from the existing teaching or tutorial slides, created by colleagues in CMU, Stanford, UIUC, UC Berkeley, GaTech, Microsoft, Google, Meta, DeepMind, NVIDIA, and more. The instructor owes many thanks for their generosity of sharing those materials publicly.