Research 2.0Harnessing Collective

Intelligence

Yung-Yu Chuang 莊永裕Communication &

Multimedia LaboratoryNational 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 2.0Web2.0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 1.0 Web 2.0DoubleClick Google AdSensemp3.com NapsterBritannica online wikipediapersonal website bloggingpublishing participation

The long tail80-20 ruleLaw of the vital few

Web 2.0 involves all peopleand 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

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 2002.

• 541 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

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