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Recent Advances in Computer Vision

         Ming-Hsuan Yang
Computer vision
• Holly grail – tell a story from an image
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
1970s
1980s
1990s


        • Face detection
        • Particle filter
        • Pfinder
        • Normalized cut
2000s
• SIFT
  –   Mosaicing, panorama
  –   Object recognition
  –   Photo tourism, photosynth
  –   Human detection

• Adaboost-based face detector
Related topics
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
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
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, …
Conference organization
• General chairs: administration
• Program chairs: handling papers
• Area chairs:
  –   Assign reviewers
  –   Read reviews and rebuttals
  –   Consolidation reports
  –   Recommendation
• Reviewers
• Authors
Review process
• Submission
• CVPR/ECCV/ICCV
  – Double blind review
  – Program chairs: assign papers to area chairs
  – Area chairs: assign papers to reviewers
• Rebuttal
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
Conference acceptance rates
•   ICCV/CVPR/ECCV: ~ 30%
•   ACCV (2009): ~ 30%
•   NIPS: ~ 30%
•   BMVC: ~ 40%
•   ICIP: ~ 45%
•   ICPR: ~ 55%

• Disclaimer
    – low acceptance rate = high quality?
CVPR
 Submission             Oral




              Overall
ICCV
 Submission             Oral




              Overall
ECCV
 Submission             Oral




              Overall
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)
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
• …
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
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
IJCV/CVIU review process
• Similar formats
• CVIU has roughly the same turn-around
  time as PAMI
• IJCV tends to have longer turn-around
  time
Journal acceptance rate
• PAMI, IJCV: ~ 30%
• CVIU: ~ 30%
Tools
• Google scholar, citeseer,
• h-index
• Software: publish or perish

• Disclaimer:
  – h index = significance?
  – # of citation = significance
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
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
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?
Experimental validation
•   Common data set
•   Killer data set
•   Large scale experiment
•   Evaluation metric
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?
Writing
•   Clear presentation
•   Terse
•   Careful about wording
•   Make claims with strong evidence
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
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
Challenging issues
•   Large scale
•   Unconstrained
•   Real-time
•   Robustness
•   Recover from failure – graceful dead
Some hot topics
•   Object recognition, categorization
•   Internet scale image search
•   Video search
•   Human detection
•   3D human pose estimation
•   Computational photography
•   Scene understanding
Some hot tools
•   Prior
•   Context
•   Sparse representation
•   Multiple instance learning
•   Online learning
•   Convex optimization
•   Constraint
•   Hashing
Prior




        Torralba and Sinha ICCV 01
Prior




        Heitz and Koller ECCV 08
Prior




   Jia CVPR 08   He et al. CVPR 09
Scene understanding




          Leibe et al. CVPR 07
Image search




          Wu et al. CVPR 09
Computational photography




        Johnson and Adelson et al. CVPR 09
Computational photography




           Ahuja et al.

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Recent Advances in Computer Vision

  • 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
  • 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
  • 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