COMPSCI 670 | Schedule

Date	Lecture	Readings	Logistics
9/4	Lecture #1 (Grant): Introduction
9/9	Lecture #2 (Grant): ConvNets	[MNIST] Gradient-Based Learning Applied to Document Recognition Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner 1998 [LeNet] Backpropagation applied to handwritten zip code recognition Yann LeCun, Bernhard Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne Hubbard, and Lawrence D. Jackel 1989
9/11	Lecture #3 (Grant): ConvNets	ImageNet: A Large-Scale Hierarchical Image Database Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei 2009 [AlexNet] Imagenet classification with deep convolutional neural networks Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton 2012 [VGG] Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman 2014
9/16	Lecture #4 (Grant): ConvNets	[ResNet] Deep residual learning for image recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun 2016 MobileNets: efficient convolutional neural networks for mobile vision applications Andrew Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam 2017	Proposal Ideas Due
9/18	Lecture #5 (Grant): Transformers	[ViT] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby 2020 Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 2021
9/22			Team Formation Deadline
9/23	Lecture #6 (Grant): Self-Supervision	[MAE] Masked Autoencoders Are Scalable Vision Learners Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, Ross Girshick 2021 [DINO] Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jegou, Julien Mairal, Piotr Bojanowski, Armand Joulin 2021
9/25	Lecture #7 : GenAI Workshop		Rising Stars in GenAI
9/29			Project Proposal Report and Presentations Due
9/30	Lecture #8 (Students): Project Proposal Presentations
10/2	Lecture #9 (Students): Project Proposal Presentations
10/7	Lecture #10 (Grant): Detection	COCO: Microsoft COCO: Common Objects in Context Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick 2014 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun 2015 [YOLO] You Only Look Once: Unified, Real-Time Object Detection Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi 2015
10/9	Lecture #11 (Grant): Segmentation	Mask-RCNN Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick 2017 [SAM] Segment Anything Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C. Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick 2023 [SAM2] Segment Anything in Images and Videos Nikhila Ravi, Valentin Gabeur, Yuan-Ting Hu, Ronghang Hu, Chaitanya Ryali, Tengyu Ma, Haitham Khedr, Roman Rädle, Chloe Rolland, Laura Gustafson, Eric Mintun, Junting Pan, Kalyan Vasudev Alwala, Nicolas Carion, Chao-Yuan Wu, Ross Girshick, Piotr Dollár, Christoph Feichtenhofer 2024
10/15	Lecture #12 (Grant): Semi-Supervised Learning	Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results Antti Tarvainen, Harri Valpola 2017 Realistic Evaluation of Deep Semi-Supervised Learning Algorithms Avital Oliver, Augustus Odena, Colin Raffel, Ekin D. Cubuk, Ian J. Goodfellow 2018
10/16	Lecture #13 (Grant): Semi-Supervised Learning	MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin A Raffel 2019 FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D. Cubuk, Alex Kurakin, Han Zhang, Colin Raffel 2020
10/21	Lecture #14 (Grant): Multilabel Classification & Calibration	Asymmetric Loss For Multi-Label Classification Emanuel Ben-Baruch, Tal Ridnik, Nadav Zamir, Asaf Noy, Itamar Friedman, Matan Protter, Lihi Zelnik-Manor 2020 Towards Calibrated Multi-label Deep Neural Networks Jiacheng Cheng, Nuno Vasconcelos 2024
10/23	Lecture #15 (Grant): Current Backbones	Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles Chaitanya Ryali, Yuan-Ting Hu, Daniel Bolya, Chen Wei, Haoqi Fan, Po-Yao Huang, Vaibhav Aggarwal, Arkabandhu Chowdhury, Omid Poursaeed, Judy Hoffman, Jitendra Malik, Yanghao Li, Christoph Feichtenhofer 2023 RepViT: Revisiting Mobile CNN From ViT Perspective Ao Wang, Hui Chen, Zijia Lin, Jungong Han, Guiguang Ding 2023
10/28	Lecture #16 (Grant): Vision + Language	[CLIP] Learning Transferable Visual Models From Natural Language Supervision Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever 2021 What does CLIP know about a red circle? Visual prompt engineering for VLMs Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi 2023
10/30	Lecture #17 (Grant): Vision + Language	Improved Zero-Shot Classification by Adapting VLMs with Text Descriptions Oindrila Saha, Grant Van Horn, Subhransu Maji 2024 Boosting Vision-Language Models with Transduction Maxime Zanella, Benoît Gérin, Ismail Ben Ayed 2024
11/3			Project Midpoint Report and Presentations Due
11/4	Lecture #18 (Students): Project Midpoint Presentations
11/6	Lecture #19 (Students): Project Midpoint Presentations
11/13	Lecture #20 (Grant): Midpoint Report Feedback
11/18	Lecture #21 (Grant): Style Transfer	Image Style Transfer Using Convolutional Neural Networks Leon Gatys, Alexander Ecker, Matthias Bethge 2016	Poster Presenation Info
11/20	Lecture #22 (Grant): Textures & Image Priors	Texture Networks: Feed-forward Synthesis of Textures and Stylized Images Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, Victor Lempitsky 2016 Deep Image Prior Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky 2017
11/25	Lecture #23 (Grant): No Class
12/2	Lecture #24 (Grant): Radiance Fields	NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng 2020 3D Gaussian Splatting for Real-Time Radiance Field Rendering Bernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George Drettakis 2023
12/4	Lecture #25 (Grant): Diffusion Models	Denoising Diffusion Probabilistic Models Jonathan Ho, Ajay Jain, Pieter Abbeel 2020 Video Diffusion Models Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet 2022
12/8			Project Report Due
12/9	Lecture #26 (Grant): Poster Presentations