Research 2.0 Harnessing Collective Intelligence Yung-Yu Chuang 莊永裕 Communication & Multimedia Laboratory National Taiwan University

Research 2.0 Research 2.0 = Research based on the concept of Web 2.0 Similar idea/term was proposed by Harry Shum of MSRA Observations from vision and multimedia research

Web 2.0 Web2.0 的精神在於 ” 肯定網路上不特定多數人並非 被動的服務享受者，而是主動的創作者，並積極地 開發技術或服務，鼓勵這些人參與。 ” 梅田望夫 Web 1.0Web 2.0 DoubleClickGoogle AdSense mp3.comNapster Britannica onlinewikipedia personal websiteblogging publishingparticipation

The long tail rule Law of the vital few Web 2.0 involves all people and shifts the authority. Books, media, software…

Web 2.0 (Tim O’Reilly) The web as platform Data is the next Intel Inside Harnessing collective intelligence …

Research 2.0 Data, paper and code are on the web –Benchmark becomes more and more important. Sharing your data and code is likely to make your research more influential.

Stereo problem

Middlebury stereo page

Performance for over 40 methods were reported; 36 of them were submitted by other researchers.

Middlebury stereo page A review paper along with a benchmark was published in IJCV citations since then according to Google scholar.

LIBSVM (C.J.Lin at NTU) 873 citations since 2001 according to Google scholar. SVM is not necessarily the best tool for classification. Its popularity could gain from some robust and easy-to-use tools.

Research 2.0 Data, paper and code are on the web –Benchmark becomes more and more important. Sharing your data and code is likely to make your research more influential.

Research 2.0 Data, paper and code are on the web –Benchmark becomes more and more important. Sharing your data and code is likely to make your research more influential. Explore vast amount of (noisy) data –Statistical approaches (machine learning, data mining, information retrieval)

Landmark project What are the text keywords for landmarks? What are the visual keywords associated with landmarks?

Research 2.0 Data, paper and code are on the web –Benchmark becomes more and more important. Sharing your data and code is likely to make your research more influential. Explore vast amount of (noisy) data –Statistical approaches (machine learning, data mining, information retrieval) Utilize collective intelligence –Good designs and motivations encourage people to make contributions

What can users contributes? YouTube/flickr: media and tags Wikipedia: knowledge Amazon: reviews/comments Connextions: courses MIT’s openmid: common sense Human computation cycles

Application to ROI We have applied this idea to ROI research. There is no benchmark There is no evaluation There is no example-based approach

What is ROI?

How to detect? Heuristics –Contrast –Face –Text –Shape …

How to detect? Heuristics –Contrast –Face –Text –Shape … User labeling –Manual –Eye tracker …

Our approach Collect large amount of ground truth Evaluate existing algorithms A learning-based algorithm

Conclusions Because of Internet’s paradigm shift, what are new research possibilities? The answers are left to you.