Time/Place: TR 09:40-10:55 in
315 B&L 205 Meliora
TA: Lele Chen
Office Hours: WF 11:10-noon in 4205 Wegmans Hall
The syllabus is below.
This course covers advanced research topics in computer vision with an emphasis on learning structured representations and embeddings. Approaches for learning from unimodal (e.g., images and videos), and multimodal data (e.g., vision and language, vision and audio) will be covered and include topics from structured predications, deep learning and others. The course will be a mix of lecture, student presentation and discussion. Prerequisites: CSC 249/449 or CSC 246/446 or CSC 298/578 (Deep Learning and Graphical Models) or permission of the instructor.
PhD Students: gain research experience in computer vision; MS Students: get prepared for a software engineer career in computer vision and artificial intelligence.
Find our class page here.
This is a tentative schedule of the course.
Course materials will be posted on Blackboard.
Red: lecture; Blue: student presentation; Green: project presentation; Grey: no class or cancelled class.
|1||R 8/29||Overview, Boot Camp: Neural Networks||Instructor|
|2||T 9/3||Boot Camp: Convolutional Neural Networks||Instructor|
|2||R 9/5||Boot Camp: Recurrent Neural Networks||Instructor|
|3||T 9/10||Tutorial: PyTorch, Google Cloud, Training GANs||TA|
|3||R 9/12||Chen et al. CVPR 2019: Hierarchical Cross-modal Talking Face Generation with Dynamic Pixel-wise Loss||TA|
|4||T 9/17||Selvaraju et al. ICCV 2017: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization||Guofeng, Haomin|
|4||R 9/19||Fong and Vedaldi ICCV 2017: Interpretable Explanations of Black Boxes by Meaningful Perturbation||Max, Tushar|
|5||T 9/24||Bau et al. ICLR 2019: GAN Dissection: Visualizing and Understanding Generative Adversarial Networks||Sizhe, Zhaoxiong|
|5||R 9/26||Liu et al. CVPR 2019: Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation||Guo, Ibrahim|
|6||T 10/1||Xie et al. ICCV 2019: Exploring Randomly Wired Neural Networks for Image Recognition||Nageswara, Xiaoning|
|6||R 10/3||Cubuk et al. CVPR 2019: AutoAugment: Learning Augmentation Policies from Data||Tushar, Zhenfei|
|7||T 10/8||Shevlev and Avidan CVPR 2019: Co-Occurrence Neural Network||Max, Zhaoxiong|
|7||R 10/10||Li et al. CVPR 2019: Finding Task-Relevant Features for Few-Shot Learning by Category Traversal||Jianyuan, Purvanshi|
|8||T 10/15||Fall Break|
|8||R 10/17||Wang et al. CVPR 2019: TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning||Guofeng, Sizhe|
|9||T 10/22||Alfassy et al. CVPR 2019: LaSO: Label-Set Operations networks for multi-label few-shot learning||Xiaoning, Zhenfei|
|9||R 10/24||Wu et al. CVPR 2019: Large Scale Incremental Learning||Ibrahim, Max|
|10||T 10/29||Tung et al. CVPR 2019: Learning Spatial Common Sense with Geometry-Aware Recurrent Networks||Guo, Haomin|
|10||R 10/31||Li et al. ICCV 2019: Expectation-Maximization Attention Networks for Semantic Segmentation||Nageswara, Sizhe|
|11||T 11/5||Zellers et al. CVPR 2018: Neural motifs: Scene graph parsing with global context||Jianyuan, Zhaoxiong|
|11||R 11/7||Kolkin et al. CVPR 2019: Style Transfer by Relaxed Optimal Transport and Self-Similarity||Ibrahim, Xiaoning|
|12||T 11/12||Wu et al. ArXiv 2019: A Comprehensive Survey on Graph Neural Networks||Max, Purvanshi|
|12||R 11/14||Wang et al. CVPR 2019: Learning Correspondence from the Cycle-Consistency of Time||Haomin, Zhenfei|
|13||T 11/19||Dwibedi et al. CVPR 2019: Temporal Cycle-Consistency Learning||Jianyuan, Nageswara|
|13||R 11/21||Sun et al. ICCV 2019: VideoBERT: A Joint Model for Video and Language Representation Learning||Guo, Tushar|
|14||T 11/26||Zellers et al. CVPR 2019: From Recognition to Cognition: Visual Commonsense Reasoning||Guofeng, Purvanshi|
|14||R 11/28||Thanksgiving Recess|
|15||T 12/3||Mao et al. ICLR 2019: The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences from Natural Supervision||Sizhe, Zhenfei|
|15||R 12/5||Project Presentation
Group 1: Purvanshi, Max, Guo, Sizhe, Tushar, Xiaoning, Zhenfei
|16||T 12/10||Project Presentation
Group 2: Guofeng, Ibrahim, Jianyuan, Negaswara, Zhaoxiong
All assignments should be submitted on Blackboard and no late work will be accepted.
Textbooks: There are no required textbooks. We will read papers from CVPR, NIPS etc.
Code of Conduct: Check here for classroom "etiquette" expectations.
Academic honesty: All assignments and activities associated with this course must be performed in accordance with the University of Rochester's Academic Honesty Policy. More information is available at here. You are expected to read, understand and follow the policy.
Disabilities: If you need an accommodation based on the impact of a disability, please contact me privately during the first two weeks to discuss your specific needs. Alternatively, contact Center for Excellence in Teaching and Learning at 585-275-9049 in room 1-154 Dewey Hall to coordinate reasonable accommodations.