16-721: Learning-based Methods in Vision

1. 16-721: Learning-based Methods in Vision • Staff: • Instructor: Alexei (Alyosha) Efros (efros@cs), 4207 NSH • TA: Tomasz Malisiewicz (tomasz@cmu), Smith Hall 236 • Web Page: • http://www.cs.cmu.edu/~efros/courses/LBMV09/

2. Today • Introduction • Why This Course? • Administrative stuff • Overview of the course

3. A bit about me • Alexei (Alyosha) Efros • Relatively new faculty (RI/CSD) • Ph.D 2003, from UC Berkeley (signed by Arnie!) • Research Fellow, University of Oxford, ’03-’04 • Teaching • The plan is to have fun and learn cool things, both you and me! • Social warning: I don’t see well • Research • Vision, Graphics, Data-driven “stuff”

4. PhD Thesis on Texture and Action Synthesis Smart Erase button in Microsoft Digital Image Pro: Antonio Criminisi’s son cannot walk but he can fly

5. Why this class? • The Old Days™: • 1. Graduate Computer Vision • 2. Advanced Machine Perception

6. Why this class? • The New and Improved Days: • 1. Graduate Computer Vision • 2. Advanced Machine Perception • Physics-based Methods in Vision • Geometry-based Methods in Vision • Learning-based Methods in Vision

7. The Hip & Trendy Learning Describing Visual Scenes using Transformed Dirichlet Processes. E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. NIPS, Dec. 2005.

8. Learning as Last Resort

9. Learning as Last Resort • EXAMPLE: • Recovering 3D geometry from single 2D projection • Infinite number of possible solutions! from [Sinha and Adelson 1993]

10. Learning-based Methods in Vision • This class is about trying to solve problems that do not have a solution! • Don’t tell your mathematician frineds! • This will be done using Data: • E.g. what happened before is likely to happen again • Google Intelligence (GI): The AI for the post-modern world! • Note: this is not quite statistics • Why is this even worthwhile? • Even a decade ago at ICCV99 Faugeras claimed it wasn’t!

11. The Vision Story Begins… • “What does it mean, to see? The plain man's answer (and Aristotle's, too). would be, to know what is where by looking.” • -- David Marr, Vision (1982)

12. depth map Vision: a split personality • “What does it mean, to see? The plain man's answer (and Aristotle's, too). would be, to know what is where by looking. In other words, vision is the process of discovering from images what is present in the world, and where it is.” • Answer #1: pixel of brightness 243 at position (124,54) • …and depth .7 meters • Answer #2: looks like bottom edge of whiteboard showing at the top of the image • Which do we want? • Is the difference just a matter of scale?

13. Measurement vs. Perception

14. Brightness: Measurement vs. Perception

15. Brightness: Measurement vs. Perception Proof!

16. Lengths: Measurement vs. Perception Müller-Lyer Illusion http://www.michaelbach.de/ot/sze_muelue/index.html

17. Vision as Measurement Device Real-time stereo on Mars Physics-based Vision Virtualized Reality Structure from Motion

18. …but why do Learning for Vision? • “What if I don’t care about this wishy-washy human perception stuff? I just want to make my robot go!” • Small Reason: • For measurement, other sensors are often better (in DARPA Grand Challenge, vision was barely used!) • For navigation, you still need to learn! • Big Reason: • The goals of computer vision (what + where) are in terms of what humans care about.

19. So what do humans care about? slide by Fei Fei, Fergus & Torralba

20. Verification: is that a bus? slide by Fei Fei, Fergus & Torralba

21. Detection: are there cars? slide by Fei Fei, Fergus & Torralba

22. Identification: is that a picture of Mao? slide by Fei Fei, Fergus & Torralba

23. Object categorization sky building flag face banner wall street lamp bus bus cars slide by Fei Fei, Fergus & Torralba

24. Scene and context categorization • outdoor • city • traffic • … slide by Fei Fei, Fergus & Torralba

25. Rough 3D layout, depth ordering slide by Fei Fei, Fergus & Torralba

26. Challenges 1: view point variation slide by Fei Fei, Fergus & Torralba Michelangelo 1475-1564

27. Challenges 2: illumination slide credit: S. Ullman

28. Challenges 3: occlusion slide by Fei Fei, Fergus & Torralba Magritte, 1957

29. Challenges 4: scale slide by Fei Fei, Fergus & Torralba

30. Challenges 5: deformation slide by Fei Fei, Fergus & Torralba Xu, Beihong 1943

31. Challenges 6: background clutter slide by Fei Fei, Fergus & Torralba Klimt, 1913

32. Challenges 7: object intra-class variation slide by Fei-Fei, Fergus & Torralba

33. Challenges 8: local ambiguity slide by Fei-Fei, Fergus & Torralba

34. Challenges 9: the world behind the image

35. In this course, we will: Take a few baby steps…

36. Goals • Read some interesting papers together • Learn something new: both you and me! • Get up to speed on big chunk of vision research • understand 70% of CVPR papers! • Use learninig-based vision in your own work • Try your hand in a large vision project • Learn how to speak • Learn how think critically about papers

37. Course Organization • Requirements: • Class Participation (33%) • Keep annotated bibliography • Post on the Class Blog before each class • Ask questions / debate / flight / be involved! • Two Projects (66%) • Analysis Project • Implement and Evaluate paper and present it in class • Must talk to me AT LEAST 2 weeks beforehand! • Synthesis Project • Can be done solo or in groups of 2 • Regular meetings • Must use lots of data

38. Class Participation • Keep annotated bibliography of papers you read (always a good idea!). The format is up to you. At least, it needs to have: • Summary of key points • A few Interesting insights, “aha moments”, keen observations, etc. • Weaknesses of approach. Unanswered questions. Areas of further investigation, improvement. • Before each class: • Submit your summary for current paper(s) in hard copy (printout/xerox) • Submit a comment on the Class Blog • ask a question, answer a question, post your thoughts,praise, criticism, start a discussion, etc.

39. Analysis Project • Pick a paper / set of papers from the list • Understand it as if you were the author • Re-implement it • If there is code, understand the code completely • Run it on data the same data (you can contact authors for data and even code sometimes) • Understand it better than the author • Run it on LOTS of new data (e.g. LabelMe dataset, Flickr dataset, etc, etc) • Figure out how it succeeds, how it fails, where it fails, and, most importantly WHY it fails • Look at which parts of the code do the real work, and which parts are just window-dressing • Maybe suggest directions for improvement. • Prepare an amazing 1hr presentation • Discuss with me twice – once when you start the project, 3 days before the presentation

40. Synthesis Project • Can grow out of analysis project, or your own research • But it needs to use large amounts of data! • 1-2 people per project. • Project proposals in a few weeks. • Project presentations at the end of semester. • Results presented as a CVPR-format paper. • Hopefully, a few papers may be submitted to conferences.

41. End of Semester Awards • We will vote for: • Best Analysis Project • Best Synthesis Project • Best Blog Comment  • Prize: dinner in a French restaurant in Paris (transportation not included!) or some other worthy prizes