1. Recent Advances in Computer Vision
Ming-Hsuan Yang

2. Computer vision
• Holly grail – tell a story from an image

3. History
• “In the 1960s, almost no one realized that
machine vision was difficult.” – David Marr,
1982
• Marvin Minsky asked Gerald Jay Sussman
to “spend the summer linking a camera to
a computer and getting the computer to
describe what it saw” – Crevier, 1993
• 40+ years later, we are still working on this

4. 1970s

5. 1980s

6. 1990s
• Face detection
• Particle filter
• Pfinder
• Normalized cut

7. 2000s
• SIFT
– Mosaicing, panorama
– Object recognition
– Photo tourism, photosynth
– Human detection
• Adaboost-based face detector

8. Related topics

9. Conferences
• CVPR – Computer Vision and Pattern
Recognition, since 1983
– Annual, held in US
• ICCV – International Conference on
Computer Vision, since 1987
– Every other year, alternate in 3 continents
• ECCV – European Conference on
Computer Vision, since 1990
– Every other year, held in Europe

10. Conferences (cont’d)
• ACCV – Asian Conference on Computer
Vision
• BMVC – British Machine Vision
Conference
• ICPR – International Conference on
Pattern Recognition
• SIGGRAPH
• NIPS – Neural Information Processing
Systems

11. Conferences (cont’d)
• MICCAI – Medical Image Computing and
Computer-Assisted Intervention
• ISBI – International Symposium on Biomedical
Imaging
• FG – IEEE Conference on Automatic Face and
Gesture Recognition
• ICCP, ICDR, ICVS, DAGM, CAIP, MVA, AAAI,
IJCAI, ICML, ICRA, ICASSP, ICIP, SPIE, DCC,
WACV, 3DPVT, ACM Multimedia, ICME, …

12. Conference organization
• General chairs: administration
• Program chairs: handling papers
• Area chairs:
– Assign reviewers
– Read reviews and rebuttals
– Consolidation reports
– Recommendation
• Reviewers
• Authors

13. Review process
• Submission
• CVPR/ECCV/ICCV
– Double blind review
– Program chairs: assign papers to area chairs
– Area chairs: assign papers to reviewers
• Rebuttal

14. Area chair meetings
• 2 day meetings
• Several panels
• Each paper is reviewed by at least 2 area
chairs
• Buddy system
• Area chair make recommendations
• Program chairs make final decisions

15. Conference acceptance rates
• ICCV/CVPR/ECCV: ~ 30%
• ACCV (2009): ~ 30%
• NIPS: ~ 30%
• BMVC: ~ 40%
• ICIP: ~ 45%
• ICPR: ~ 55%
• Disclaimer
– low acceptance rate = high quality?

16. CVPR
Submission Oral
Overall

17. ICCV
Submission Oral
Overall

18. ECCV
Submission Oral
Overall

19. Journals
• PAMI – IEEE Transactions on Pattern
Analysis and Machine Intelligence, since
1979 (impact factor: 5.96, #1 in all engineering
and AI, top-ranked IEEE and CS journal)
• IJCV – International Journal on Computer
Vision, since 1988 (impact factor: 5.36, #2 in
all engineering and AI)
• CVIU – Computer Vision and Image
Understanding, since 1972 (impact factor:
2.20)

20. Journals (cont’d)
• IVC – Image and Vision Computing
• IEEE Transactions on Medical Imaging
• TIP – IEEE Transactions on Image
Processing
• MVA – Machine Vision and Applications
• PR – Pattern Recognition
• TM – IEEE Transactions on Multimedia
• …

21. PAMI review process
• Editor-in-chief (EIC) assigns papers to
associate editors (AE)
• AE assigns reviewers
• First-round review: 3-6 months
– Accept as is
– Accept with minor revision
– Major revision
– Resubmit as new
– Reject

22. PAMI review process (cont’d)
• Second-round review: 2-4 months
– Accept as is
– Accept with minor revision
– Reject
• EIC makes final decision
• Overall turn-around time: 6 to 12 months
• Rule of thumb: 30% additional work
beyond a CVPR/ICCV/ECCV paper

23. IJCV/CVIU review process
• Similar formats
• CVIU has roughly the same turn-around
time as PAMI
• IJCV tends to have longer turn-around
time

24. Journal acceptance rate
• PAMI, IJCV: ~ 30%
• CVIU: ~ 30%

25. Tools
• Google scholar, citeseer,
• h-index
• Software: publish or perish
• Disclaimer:
– h index = significance?
– # of citation = significance

26. How to get your papers rejected?
• Refer to Jim Kajia (SIGGRAPH 93 papers
chair): How to get your SIGGRAPH paper
rejected?
• Do not
– Pay attention to review process
– Put yourself as a reviewer perspective
– Put the work in right context
– Carry out sufficient amount of experiments
– Compare with state-of-the-art algorithms
– Pay attention to writing

27. Pay attention to review process
• Learn how others/you can pick apart a
paper
• Learn from other’s mistakes
• Learn how to write good papers
• Learn what it takes to get a paper
published

28. Put yourself as reviewer
• What are the contributions?
• Does it advance the science in the filed?
• Why you should accept this paper?
• Is this paper a case study?
• Is this paper interesting?
• What is the audience?
• Does anyone care about this work?

29. Experimental validation
• Common data set
• Killer data set
• Large scale experiment
• Evaluation metric

30. Compare with state of the art
• Do your homework
• Need to know what is out there
• Need to show why one’s method
outperforms others, and in what way?
– speed?
– accuracy?
– easy to implement?
– general application?

31. Writing
• Clear presentation
• Terse
• Careful about wording
• Make claims with strong evidence

32. Review form
• Summary
• Overall Rating
– Definite accept, weakly accept, borderline, weakly reject, definite
reject
• Novelty
– Very original, original, minor originality, has been done before
• Importance/relevance
– Of broad interest, interesting to a subarea, interesting only to a
small number of attendees, out of CVPR scope

33. Review form (cont’d)
• Clarity of presentation
– Reads very well, is clear enough, difficult to read, unreadable
• Technical correctness
– Definite correct, probably correct but did not check completely,
contains rectifiable errors, has major problems
• Experimental validation
– Excellent validation or N/A (a theoretical paper), limited but
convincing, lacking in some aspects, insufficient validation
• Additional comments
• Reviewer’s name

34. Challenging issues
• Large scale
• Unconstrained
• Real-time
• Robustness
• Recover from failure – graceful dead

35. Some hot topics
• Object recognition, categorization
• Internet scale image search
• Video search
• Human detection
• 3D human pose estimation
• Computational photography
• Scene understanding

36. Some hot tools
• Prior
• Context
• Sparse representation
• Multiple instance learning
• Online learning
• Convex optimization
• Constraint
• Hashing

37. Prior
Torralba and Sinha ICCV 01

38. Prior
Heitz and Koller ECCV 08

39. Prior
Jia CVPR 08 He et al. CVPR 09

40. Scene understanding
Leibe et al. CVPR 07

41. Image search
Wu et al. CVPR 09

42. Computational photography
Johnson and Adelson et al. CVPR 09

43. Computational photography
Ahuja et al.