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<h2>Fall 21 - Advanced Topics in Computer Vision (ECE 381V/CS 395T)</h2>
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<div class="trend-contents">
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<b style="color:rgb(68, 68, 68); font-size:22px"></b>
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<p><div class="column_1">Term</div>
<div class="column_2">Fall 2021</div></p>
<p><div class="column_1">Meeting times and location</div>
<div class="column_2">MW 1:30-3:00pm (EER 1.518)</div></p>
</div>
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<b style="color:rgb(68, 68, 68); font-size:22px">Course Description and Prerequisites</b>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
<p>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.</p>
<p>Students should have taken the following courses or equivalent: Convex Optimization (381K-18), and Probability & Stochastic Process I (381J).</p>
<p>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).</p>
<p>Coding experiences with Python are necessary and assumed. Previous knowledge of C/C++, MATLAB or Tensorflow is very helpful, but not necessary.</p>
</div>
<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px; padding-bottom:50px">Instructor and TA Information</b>
<div style="padding-bottom:50px" class="trend-contents">
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">Instructor Name</div> <div class="column_2">Dr. Zhangyang (Atlas) Wang</div></p>
<p><div class="column_1">Telephone number</div> <div class="column_2">512-471-1866</div></p>
<p><div class="column_1">Email address</div>
<div class="column_2"><a herf="mailto:[email protected]">[email protected]</a></div></p>
<p><div class="column_1">Office location</div>
<div class="column_2">EER 6.886</div></p>
<p><div class="column_1">TA Info: </div>
<div class="column_2"> <a href="http://www.xiaohanchen.com/">Xiaohan Chen</a>, <a herf="mailto:
[email protected]">[email protected]</a></div></p>
<p><div class="column_1">Zoom and Slack Links</div>
<div class="column_2">sent to registered students</div></p>
</div>
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<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px">Textbook and/or Resource Material</b>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
<p>This course does not follow any textbook closely. Among many recommended readings are:</p>
<ul>
<li><a href="https://www.microsoft.com/enus/
research/people/cmbishop/">Pattern Recognition and Machine Learning</a>, Christopher M. Bishop (2006).</li>
<li><a href="http://szeliski.org/Book/">Computer Vision: Algorithms and Applications</a>, Richard Szeliski (2010).</li>
<li><a href="http://www.deeplearningbook.org/">Deep Learning</a>, Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016).</li>
<li><a href="https://d2l.ai/">Diving into Deep Learning</a>, Aston Zhang, Zack Lipton, Mu Li and Alex Smola (2019).</li>
</ul>
</div>
</div>
<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px">Grading Policies</b>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
<P>Grading will be based on class participation (<B>10%</B>), one mid-term exam (<B>30%</B>), and one final project (<B>60%</B>) (middle-term progress report <B>15%</B> + presentation <B>15%</B> + final report <B>15%</B> + code review <B>15%</B>). There will be no final exam.</P>
<ul>
<li>One project to receive the Best Project Award, voted by all class members. (<b>+5%</b>)</li>
<li>Projects in the novel, interdisciplinary domains (some examples: 5G/6G telecommunication, brain-computer interface, economics & markets, COVID-19, etc.), judged by the instructor. (<b>+2%</b>)</li>
<li>For late submission, each additional late day will incur a <b>10%</b> penalty.</li>
</ul>
</div>
</div>
<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px">Course Topics</b>
<div style="color:rgb(68, 68, 68)" class="trend-contents">
<br />
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">8/25 Wednesday</div> <div class="column_2">Introduction and Computer Vision Basics <a href="Fall21/Lecture 1.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">8/30 Monday</div> <div class="column_2">Deep Learning Basics (1): Building A Deep Network</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/01 Wednesday</div> <div class="column_2">Deep Learning Basics (2): Representative Models and Tasks</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/03 Monday</div><div class="column_2">- Class Cancelled (Labor Day) - </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/08 Wednesday</div><div class="column_2">Deep Learning Basics (3): Advanced Models and Optimization <a href="Fall21/Lecture 2-4.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/13 Monday</div> <div class="column_2">Topic I: Label-Efficient Learning (1): Semi-Supervised Learning</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/15 Wednesday</div> <div class="column_2">Topic I: Label-Efficient Learning (2): Few-Shot & Active Learning </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/20 Monday</div> <div class="column_2"> Topic I: Label-Efficient Learning (3): Transfer & Multi-Task & Self-Supervised Learning <a href="Fall21/Lecture 5-7.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/22 Wednesday</div> <div class="column_2"> Topic II: Resource-Efficient Learning (1): Basic Model Compression </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/27 Monday</div> <div class="column_2">Topic II: Resource-Efficient Learning (2): Advanced Model Compression </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/29 Wednesday</div> <div class="column_2"> Topic II: Resource-Efficient Learning (3): Efficient Training and Fine-Tuning <a href="Fall21/Lecture 8-10.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/04 Monday</div> <div class="column_2">Topic III: Robust Vision (1): Image Enhancement </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/06 Wednesday</div> <div class="column_2">Topic III: Robust Vision (2): Uncertainty</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/11 Monday</div> <div class="column_2">Topic III: Robust Vision (3): Domain Generalization </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/13 Wednesday</div> <div class="column_2">Topic III: Robust Vision (4): Adversarial Robustness</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/18 Monday</div> <div class="column_2"> Topic III: Robust Vision (5): Synthetic Data <a href="Fall21/Lecture 11-15.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/20 Wednesday</div> <div class="column_2">Topic IV: Generative Models (1)</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/25 Monday</div> <div class="column_2">Topic IV: Generative Models (2) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/27 Wednesday</div> <div class="column_2">Topic IV: Generative Models (3) <a href="Fall21/Lecture 16-18.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/01 Monday</div> <div class="column_2">Topic V: AutoML and Meta Learning (1)</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/03 Wednesday</div> <div class="column_2">Topic V: AutoML and Meta Learning (2) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/08 Monday</div> <div class="column_2">Topic V: AutoML and Meta Learning (3) <a href="Fall21/Lecture 19-21.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/10 Wednesday</div> <div class="column_2">Topic VI: Vision and Language (1) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/15 Monday</div> <div class="column_2">Topic VI: Vision and Language (2)
<a href="Fall21/Lecture 22-23.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/17 Wednesday</div> <div class="column_2"> Topic VII: Transformers and MLPs in Vision (1) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/22 Monday</div> <div class="column_2"> Topic VII: Transformers and MLPs in Vision (2) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/24 Wednesday</div> <div class="column_2"> - Class Cancelled (Thanksgiving Vacation) - </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/29 Monday</div> <div class="column_2"> Topic VII: Transformers and MLPs in Vision (3) <a href="Fall21/Lecture 24-26.pdf" class="nav-link text-left">Slides</a> </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">12/01 Wednesday</div> <div class="column_2"> Class Project Presentation (1) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">12/06 Monday</div> <div class="column_2"> Class Project Presentation (2) </div></p>
</div>
</div>
</div>
</div>
</div>
<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px">Acknowledgement</b>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
<P>Many materials included in this course are adapted from the existing teaching or tutorial slides, created by colleagues in CMU, UIUC, UC Berkeley, GaTech, UVa, Microsoft, DeepMind, NVIDIA, and more. The instructor owes many thanks for their generosity of sharing those materials publicly.</P>
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