Chenliang Xu


Fall 2017 - CSC 577: Advanced Topics in Computer Vision

Instructor: Prof. Chenliang Xu
Time/Place: MW 14:00-15:15 in 601 CSB
Office Hours: MW 13:00-14:00 in 3005 Wegmans
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.


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.

W Date Topic Presenter
1 W 8/30 Overview, Boot Camp: the Basics (read)
2 M 9/04 Labor Day
2 W 9/06 Boot Camp: CNN I (read)
3 M 9/11 Boot Camp: CNN II
3 W 9/13 Boot Camp: RNN (read)
4 M 9/18 Tutorial: DL Libraries and CIRC (tutorial by Lele) Yapeng Tian, Lele Chen
4 W 9/20 Boot Camp: Graphical Models (read, more)
5 M 9/25 Huang et al. CVPR 2017: Densely Connected Convolutional Network Lele Chen
5 W 9/27 Redmon and Farhadi, CVPR 2017: YOLO9000: Better, Faster, Stronger Jiechen Liu
6 M 10/02 Reed et al. NIPS 2016: Learning What and Where to Draw Yuxiao Chen
6 W 10/04 Luc et al. ICCV 2017: Predicting Deeper into the Future of Semantic Segmentation Jie Chen
7 M 10/09 Fall Break
7 W 10/11 Shrivastava et al. CVPR 2017: Learning from Simulated and Unsupervised Images through Adversarial Training Jing Shi
8 M 10/16 Xie et al. CVPR 2017: Synthesizing Dynamic Patterns by Spatial-Temporal Generative ConvNet Zhe Hong
8 W 10/18 Project Proposal Everyone
9 M 10/23 Villegas et al. ICML 2017: Learning to Generate Long-Term Future via Hierarchical Prediction Hao Huang
9 W 10/25 Aytar et al. NIPS 2016: SoundNet: Learning Sound Representations from Unlabeled Video Ruogu Lin
10 M 10/30 Chung et al. CVPR 2017: Lip Reading Sentences in the Wild Wentian Zhao
10 W 11/01 Owens et al. CVPR 2016: Visually Indicated Sounds Zheng Zhou
11 M 11/06 Krishna et al. ICCV 2017: Dense-Captioning Events in Videos Zhongping Zhang
11 W 11/08 Hendricks et al. ICCV 2017: Localizing Moments in Video with Natural Language Gregory Hunkins
12 M 11/13 Hu et al. ICCV 2017: Learning to Reason: End-to-End Module Networks for Visual Question Answering Yapeng Tian
12 W 11/15 Castrejon et al. CVPR 2016: Learning Aligned Cross-Modal Representations from Weakly Aligned Data Zhengyuan Yang
13 M 11/20 Mattyus et al. CVPR 2016: HD Maps: Fine-grained Road Segmentation by Parsing Ground and Aerial Images Wei Zhang
13 W 11/22 Thanksgiving Recess
14 M 11/27 Yang et al. CVPR 2017: Deep Multimodal Representation Learning from Temporal Data Boyuan Tian
14 W 11/29 Cancelled
15 M 12/04 Kim et al. NIPS 2016: Mutlimodal Residual Learning for Visual QA Tianlang Chen
15 W 12/06
16 M 12/11 Project Presentation I
16 W 12/13 Project Presentation II

Work and Grade

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.