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<h2>Fall 23 - 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>
<div style="color:rgb(68, 68, 68); padding-bottom:50px" class="trend-contents">
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<p><div class="column_1">Course title</div> <div class="column_2">ECE 381V/CS 395T: Advanced Topics in Computer Vision</div></p>
<p><div class="column_1">Term</div>
<div class="column_2">Fall 2023</div></p>
<p><div class="column_1">Meeting times and location</div>
<div class="column_2"> MW 1:30pm -3:00pm (ECJ 1.318) </div></p>
<p><div class="column_1">After-class platform</div>
<div class="column_2"> Slack (link sent to registered students) </div></p>
<p><div class="column_1">Video recording</div>
<div class="column_2"> Available on Canvas </div></p>
</div>
</div>
</div>
<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: Introduction to Computer Vision (379K), 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 Information</b>
<div style="padding-bottom:50px" class="trend-contents">
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<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 hour time</div>
<div class="column_2">Thursday 2:00pm - 3:00pm </div></p>
<p><div class="column_1">Office hour location</div>
<div class="column_2">EER 6.886 (instructor office)</div></p>
</div>
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<div class="trend-contents">
<b style="color:rgb(68, 68, 68); font-size:22px; padding-bottom:1px">TA Information</b>
<div style="padding-bottom:50px" class="trend-contents">
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<p><div class="column_1">TA Name</div> <div class="column_2"><a href="">Zhangheng Li</a></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 hour time</div>
<div class="column_2"> Tuesday 11:00am - 12:00pm </div></p>
<p><div class="column_1">Office hour location</div>
<div class="column_2"> EER 3.854 </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>15%</B>), and one final project (<B>75%</B>) (milestone 1 progress report <B>15%</B> + milestone 2 progress report <B>15%</B> + presentation <B>20%</B> + final report <B>15%</B> + code review <B>10%</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">8/21 Monday</div> <div class="column_2">Topic I: Deep Vision Backbones (1): Building Blocks</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">8/23 Wednesday</div> <div class="column_2">Topic I: Deep Vision Backbones (2): Convolutional Neural Networks</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">8/28 Monday</div><div class="column_2">Topic I: Deep Vision Backbones (3): More Advanced Architectures - Part i </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">8/30 Wednesday</div><div class="column_2">Topic I: Deep Vision Backbones (4): More Advanced Architectures - Part ii <a href="Fall23/Lecture 1-4.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">9/04 Monday</div> <div class="column_2">- No Class (Labor Day) - </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/06 Wednesday</div> <div class="column_2">Topic II: Label-Efficient Learning (1): Semi-Supervised Learning </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/11 Monday</div> <div class="column_2"> Topic II: 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">9/13 Wednesday</div> <div class="column_2"> Topic II: Label-Efficient Learning (3): Transfer & Self-Supervised Learning <a href="Fall23/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">9/18 Monday</div> <div class="column_2">Topic III: Resource-Efficient Learning (1): Basic Model Compression </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/20 Wednesday</div> <div class="column_2"> Topic III: Resource-Efficient Learning (2): Sparse Neural Networks - Part i </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">9/25 Monday</div> <div class="column_2">Topic III: Resource-Efficient Learning (3): Sparse Neural Networks - Part ii <a href="Fall23/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">9/27 Wednesday</div> <div class="column_2"> Topic IV: Neural Radiance Fields (1): Single Scene Fitting [<b style="color:rgb(71, 71, 71)">guest lecture by</b> <a href="https://peihaowang.github.io/">Peihao Wang</a>] </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/02 Monday</div> <div class="column_2"> Topic IV: Neural Radiance Fields (2): Scene-Generalizable Fitting [<b style="color:rgb(71, 71, 71)">guest lecture by</b> <a href="https://ir1d.github.io/">Dejia Xu</a>] <a href="Fall23/Lecture_NeRF_Fall_2023.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 Wednesday</div> <div class="column_2"> Topic V: Robustness in Vision (1): Image Enhancement [<b style="color:rgb(71, 71, 71)">guest lecture by</b> <a href="https://ir1d.github.io/">Dejia Xu</a>]</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/09 Monday</div> <div class="column_2">Topic V: Robustness in Vision (2): Uncertainty and Domain Generalization </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/11 Wednesday</div> <div class="column_2">Topic V: Robustness in Vision (3): Adversarial Robustness <a href="Fall23/Lecture 11-14.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">10/16 Monday</div> <div class="column_2">Topic VI: AutoML and Meta Learning (1) </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/18 Wednesday</div> <div class="column_2">Topic VI: AutoML and Meta Learning (2) <a href="Fall23/Lecture 15-17.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">10/23 Monday</div> <div class="column_2"> Midterm Exam </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/25 Wednesday</div> <div class="column_2">Topic VII: Good Old Days of Generative AI (1): VAEs and GANs - Part i </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">10/30 Monday</div> <div class="column_2">Topic VII: Good Old Days of Generative AI (2): VAEs and GANs - Part ii </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/01 Wednesday</div> <div class="column_2">Topic VII: Good Old Days of Generative AI (3): VAEs and GANs - Part iii <a href="Fall23/Lecture 18-20.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">11/06 Monday</div> <div class="column_2">Topic VIII: New Age of Generative AI (1): Introduction to Diffusion Models </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/08 Wednesday</div> <div class="column_2"> Topic VIII: New Age of Generative AI (2): Deeper Dive into Diffusion Models </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/13 Monday</div> <div class="column_2"> Topic VIII: New Age of Generative AI (3): Beyond Image: Video, 3D, and Multimodal </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/15 Wednesday</div> <div class="column_2"> Topic VIII: New Age of Generative AI (4): Connecting Vision and Other "Foundation Models" </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/20 Monday</div> <div class="column_2"> - No Class (Thanksgiving Break) -</div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/22 Wednesday</div> <div class="column_2"> - No Class (Thanksgiving Break) - </div></p>
</div>
<div style="width:100%; margin-left: 0;" class="row">
<p><div class="column_1">11/27 Monday</div> <div class="column_2"> Topic VIII: New Age of Generative AI (5): the Good, the Bad, and the Future <a href="Fall23/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">11/29 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">12/04 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, 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.</P>
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