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Recent Advances in Computer Vision

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  • 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.

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