CSE 690 Internet Vision

1. CSE 690 Internet Vision Organizational Meeting Tamara Berg Assistant Professor SUNY Stony Brook

2. Course Information Instructor: Tamara Berg Email: tlberg@cs.sunysb.edu Office: 1411 Computer Science Webpage: http://tamaraberg.com/ Course: Time/Location TBD Office Hours: TBD Course Webpage: http://tamaraberg.com/teaching/Fall_08

3. About Me First year as a professor PhD from University of California, Berkeley 2007 Research Scientist at Yahoo! Research 2007-2008 Teaching First time teaching so I’ll be learning as we go along with all of you! Research Digital Media, Computer Vision, Natural Language Processing, Web Scale projects, Integrating Words & Pictures

4. About You? • Name? • Year? • MS/PhD? • Major? • Any related background in Computer Vision, Machine Learning, Graphics?

5. Automatic Image Completion James Hays, Alexei A. Efros. Scene Completion Using Millions of Photographs. SIGGRAPH 2007

6. Face Transfer "Face Swapping: Automatically Replacing Faces in Photographs,” D. Bitouk, N. Kumar, S. Dhillon, P. Belhumeur, S. K. Nayar, Siggraph 2008

7. Photo Tourism Noah Snavely, Steven M. Seitz, Richard Szeliski, "Photo tourism: Exploring photo collections in 3D,” SIGGRAPH 2006

8. Automatic Photo Pop-Up D. Hoiem, A.A. Efros, and M. Hebert, "Automatic Photo Pop-up", ACM SIGGRAPH 2005

9. Course Organization • Lectures • Student Paper Presentations • Course Project

10. Your Responsibilities • Attend lectures • Read assigned papers before each class and write down at least a few questions • Participate in discussions! • Student Paper Presentations • Course project • Have fun! New and exciting area of research with broad industrial applications.

11. Lecture Topics Visual and Multi-Media Data Computational Photography Text Based Retrieval Exemplar Based Retrieval Photo Quality for Retrieval Combining Words & Pictures Places Objects, People & Events The role of Social Networks & Human Interaction Including cutting edge research in: Computer Vision, Graphics, Multimedia, Information Retrieval, Machine Learning, HCI

12. Student Paper Presentations • You be the professor for a day • Prepare a presentation and lead a discussion on a few of the assigned research papers including discussion questions, pros/cons etc • Present a demo of the system if one is available • Feel free to run your presentation by me in office hours beforehand

13. Project Course will be project focused You will pick a project idea near the beginning of the semester - I can help suggest or refine project ideas in office hours. Projects can be implementing/modifying one of the papers we discuss or original research There will be a few status updates over the semester where you will briefly describe progress and we can provide suggestions Projects can be completed alone or in pairs Hopefully some of these projects can be submitted to a conference!

14. Worried about the material? • I will present a summary of useful knowledge, algorithms and related techniques near the beginning of the semester. • This should enable you to understand most of the research papers we will read. • If you are still worried, feel free to come see me in office hours.

15. Lectures 2 classes per week, 1:20 long Discuss 1-2 papers per class 1 class per week, 2:30 long Discuss ~3 papers per class Informal discussions, “brown bag” seminar?

16. Lecture Times?

17. For next class – Data! Please Read (links will be posted on course webpage): 80 million tiny images: a large dataset for non-parametric object and scene recognition

18. Course Information Instructor: Tamara Berg Email: tlberg@cs.sunysb.edu Office: 1411 Computer Science Webpage: http://tamaraberg.com/ Course Time: TBD Course Location: Will be posted on webpage Office Hours: TBD Course Webpage: http://tamaraberg.com/teaching/Fall_08