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c ontext networks

c ontext networks. arjun satish arjun@uci.edu. real world AI problems. autonomous cars. tagging faces in photos. Barack. Michell e. Malia. Sasha. streets can be chaotic. photos contain unknown faces. real world AI problems. large and diverse search spaces.

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c ontext networks

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  1. context networks arjunsatish arjun@uci.edu

  2. real world AI problems autonomous cars tagging faces in photos Barack Michelle Malia Sasha

  3. streets can be chaotic

  4. photos contain unknown faces

  5. real world AI problems • large and diverse search spaces. • operating in this search space: non-trivial • not always predictable

  6. problem focus: tagging faces in personal photos prune search spaces for real world AI problems

  7. personal photos

  8. who is in this photo? Eldar Shaddin Ehsan Polina Arjun Kim Friends

  9. who is in this photo? Ish Setareh Mingyan Colleagues

  10. who is in this photo? Atish Danupon Jennie Arjun Conference

  11. one more: who is in this photo?

  12. i don’t know information about the photographer is critical in tagging photos

  13. to tag a photo taken in Los Angeles

  14. people in New York

  15. events around the world

  16. are irrelevant • only those people who are close to the camera can appear in the pictures. • if we had a perfect model of all real world events, its participants occurring in the world, then we can simply query it to find the smallest set of candidates. • we refer to such models as context networks.

  17. context network concert

  18. context

  19. idea: such models can be built from commonly available data sources

  20. progressively discovery context public data social data personal data metadata data

  21. progressive discovery • my algorithm to discover context from various data sources to construct real world models • its independent of the types of sources used • guided by an ontologies and other representation of real world knowledge. • can be applied to any AI problem which can benefit from such models

  22. example #1

  23. who could this be?

  24. step #1: what do we know? • From photo: • timestamp (EXIF) • location (EXIF) • owner (of the photo) owner : arjun

  25. query: what was arjun doing at this time and location? System queries all possible sources to find results to discover:

  26. the context network looks like event at time, T at location, L attendees subevent at time, t owner-of at location, l

  27. with this enhanced context • the face tagger needs to work on only three candidates to find the correct tag event at time, T attendees at location, L subevent at time, t owner-of at location, l

  28. notes • context network was easily constructed by looking at owner’s personal calendar only. • context network reduced complexity of face tagging from thousands of people to 3 candidates.

  29. example #2

  30. who could this be?

  31. step #1: what do we know? • From photo: • timestamp (EXIF) • location (EXIF) • owner (who took this photo) owner : ramesh

  32. Query: what was ramesh doing at this time and location? • System queries all possible sources to find results to discover:

  33. this photo was taken at a conference at time, T conference (ICMR 2013) at location, L attendee subevent at time, t owner-of at location, l

  34. our ontology says: • any conference has • keynote events, • sessions • each of them containing many talks • some people organize, some present, others attend. • lunch/coffee breaks • and a possible banquet at the very end • and it usually runs into 3 or 4 days • it always happens at a single place.

  35. next query: find conference subevents at time t. • the keynote event was occurring at this time. • source: conference schedule.

  36. the context network now becomes at time, T conference (ICMR 2013) at location, L attendee subevent presenter at time, Tk keynote at location, Lk organizer subevent owner-of at time, t at location, l

  37. invoke face verification algorithms • face.com, for example keynote presenter subevent organizer

  38. invoke face verification algorithms • to check who is in the photo keynote presenter subevent organizer

  39. notes • we reduced the search space from thousands of people to 2-3 candidates. • work of face tagging algorithms are simplified by over 99%. • context network was built using EXIF, personal information and conference calendar. • note the different sources used.

  40. conclusion • context networks construct models of the real world. • they can be constructed from various common data sources. • without being tied to any specific set of sources. • they can be applied to many problems (only face tagging shown in these slides)

  41. more examples • more examples: • http://www.ics.uci.edu/~arjun/cuenet/icmr-demo/ • details on CueNet can be found at: • http://www.ics.uci.edu/~arjun/docs/cuenet.pdf

  42. thanks for watching! arjun@uci.edu

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