Advanced Computer Vision

1. Advanced Computer Vision Devi Parikh Electrical and Computer Engineering

2. Plan for today • Topic overview • Introductions • Course overview: • Logistics • Requirements • Placing this course in context of others • Plan for next lecture • Please interrupt at any time with questions or comments

3. Computer Vision Automatic understanding of images and video Computing properties of the 3D world from visual data (measurement) Algorithms and representations to allow a machine to recognize objects, people, scenes, and activities. (perception and interpretation) Algorithms to mine, search, and interact with visual data (search and organization) Kristen Grauman

4. What does recognition involve? Fei-Fei Li

5. Detection: are there people?

6. Activity: What are they doing?

7. Object categorization mountain tree building banner street lamp vendor people

8. Instance recognition Potala Palace A particular sign

9. Scene and context categorization • outdoor • city • …

10. Attribute recognition gray made of fabric crowded flat

11. Why recognition? Recognition a fundamental part of perception e.g., robots, autonomous agents Organize and give access to visual content Connect to information Detect trends and themes Where are we now? Kristen Grauman

12. We’ve come a long way…

13. We’ve come a long way…

14. We’ve come a long way…

15. Posing visual queries Yeh et al., MIT Belhumeur et al. Kooaba, Bay & Quack et al. Kristen Grauman

16. Exploring community photo collections Snavely et al. Kristen Grauman Simon & Seitz

17. Dubrovnik AutoTagger: Yunpeng Li, Noah Snavely, Dan Huttenlocher and Pascal Fua Slide credit: Devi Parikh

18. Autonomous agents able to detect objects Kristen Grauman http://www.darpa.mil/grandchallenge/gallery.asp

19. We’ve come a long way… Fischler and Elschlager, 1973

20. We’ve come a long way…

21. Challenges

22. Intra-class appearance Challenges: robustness Illumination Object pose Clutter Occlusions Viewpoint Kristen Grauman

23. Challenges: context and human experience Context cues Kristen Grauman

24. Challenges: context and human experience Function Dynamics Context cues Kristen Grauman Video credit: J. Davis

25. Challenges: scale, efficiency • Half of the cerebral cortex in primates is devoted to processing visual information • ~20 hours of video added to YouTube per minute • ~5,000 new tagged photos added to Flickr per minute • Thousands to millions of pixels in an image • 30+ degrees of freedom in the pose of articulated objects (humans) • 3,000-30,000 human recognizable object categories Kristen Grauman

26. Challenges: learning with minimal supervision More Less Unlabeled, multiple objects Cropped to object, parts and classes labeled Classes labeled, some clutter Kristen Grauman

27. Slide from Pietro Perona, 2004 Object Recognition workshop

28. Slide from Pietro Perona, 2004 Object Recognition workshop

29. Inputs in 1963… L. G. Roberts, Machine Perception of Three Dimensional Solids, Ph.D. thesis, MIT Department of Electrical Engineering, 1963. Kristen Grauman

30. Personal photo albums Movies, news, sports Medical and scientific images Surveillance and security … and inputs today Slide credit; L. Lazebnik

31. … and inputs today 916,271 titles 10 mil. videos, 65,000 added daily Images on the Web Movies, news, sports 350 mil. photos, 1 mil. added daily 1.6 bil. images indexed as of summer 2005 Satellite imagery City streets Understand and organize and index all this data!! Slide credit; L. Lazebnik

32. Introductions • Devi Parikh • Ph.D., Carnegie Mellon University, 2009 • Research Assistant Professor, TTI-Chicago, 2013 • Assistant Professor, ECE, Virginia Tech

33. Introductions • Which program are you in? • How far along? • Have you taken a computer vision course before? • Have you taken a machine learning course before? • What are you hoping to get out of this class?

34. This course ECE 6554 TR 5:00 pm to 6:15 pm Lavery Hall Room 345 Course webpage: https://filebox.ece.vt.edu/~S16ECE6554/

35. This course Focus on more advanced techniques and ideas in computer vision Presented in research papers High-level recognition problems, innovative applications.

36. Goals Understand state-of-the-art approaches Analyze and critique current approaches Identify interesting open questions Present clearly and methodically

37. Official Learning Objectives • Describe state-of-the-art approaches in object recognition and scene understanding • Discuss tools from other fields (e.g., machine learning) that are frequently used to solve computer vision problems • Implement two approaches to address important problems in computer vision • Discuss and critique research papers in computer vision • Identify open research questions in computer vision

38. Expectations Paper reviews each class [25%] Leading discussion(~ twice) on papers [15%] Presentations (~ once) [25%] Present topic Papers and background reading Project [35%] No “Assignments”, Exams, etc.

39. Prerequisites Course in computer vision Courses in machine learning is a plus

40. Paper reviews For each class Review one paper Email me reviews by noon (12:00 pm) the day of the class firstname_lastname_MM_DD.pdf I will grade a random subset in detail Skip reviews the classes you are presenting or leading discussion Late reviews will not be accepted Will drop three lowest grades on reviews

41. Paper review guidelines One page Detailed review: Brief (2-3 sentences) summary Main contribution Strengths? Weaknesses? How convincing are the experiments? Suggestions to improve them? Extensions? Applications? Additional comments, unclear points Relationships observed between the papers we are reading Most interesting thought Write in your own words Write well, proof read

42. Leading Discussion ~ One of you will be assigned to argue for the paper ~ One of you will be assigned to argue against the paper Come prepared with 5 points

43. Presentation guidelines IMPORTANT: Don’t present papers – present the topic! Do a lit review and look at background papers (e.g. “seeds / pointers for presenters”), and also more recent work. Well-organized talk, 30 minutes

44. Presentation guidelines What to cover? Topic overview, motivation One or two papers in details Problem overview, motivation Algorithm explanation, technical details Experimental set up, results Strengths, weaknesses, extensions NOT the paper the class has read. Any commonalities, important differences between techniques covered in the papers. A demo / experiment would be great See class webpage for more details.

45. Experiments Implement/download code for a main idea in the paper and evaluate it: Experiment with different types of training/testing data sets Evaluate sensitivity to important parameter settings Show an example to analyze a strength/weakness of the approach Show qualitative and quantitative results

46. Tips Look up papers and authors. Their webpage may have data, code, slides, videos, etc. Make sure talk flows well and makes sense as a whole. Cite ALL sources. Don’t forget the high-level picture. Give a very clear and well-organized and thought out talk. Will interrupt if something is not clear

47. Tips Make sure you are saying everything we need to know to understand what you are saying. Make sure you know what you are talking about. Think about your audience. Make your talks visual, animated (images, video, not lots of text).

48. Projects Possibilities: Extension of a technique studied in class Analysis and empirical evaluation of an existing technique Comparison between two approaches Design and evaluate a novel approach Be creative! Can work with a partner

49. Project timeline Project proposals (1 page) [25%] March 1st Final presentations [40%] April 19thto 26th Project video (1 minute)[35%] April 28thth

50. Implementation Use any language / platform you like No support for code / implementation issues will be provided