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Home / Proceedings / Proceedings of the AAAI Conference on Human Computation and Crowdsourcing

Cost-Effective HITs for Relative Similarity Comparisons

February 1, 2023

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Authors

Michael Wilber,Iljung Kwak,Serge Belongie

Cornell University,University of California, San Diego,Cornell University


DOI:

10.1609/hcomp.v2i1.13152


Abstract:

Similarity comparisons of the form "Is object a more similar to b than to c?" form a useful foundation in several computer vision and machine learning applications. Unfortunately, an embedding of n points is only uniquely specified by n3 triplets, making collecting every triplet an expensive task. In noticing this difficulty, other researchers investigated more intelligent triplet sampling techniques, but they do not study their effectiveness or their potential drawbacks. Although it is important to reduce the number of collected triplets to generate a good embedding, it is also important to understand how best to display a triplet collection task to the user to better respect the worker's human constraints. In this work, we explore an alternative method for collecting triplets and analyze its financial cost, collection speed, and worker happiness as a function of the final embedding quality. We propose best practices for creating cost effective human intelligence tasks for collecting triplets. We show that rather than changing the sampling algorithm, simple changes to the crowdsourcing UI can drastically decrease the cost of collecting similarity comparisons. Finally, we provide a food similarity dataset as well as the labels collected from crowd workers.

Topics: HCOMP

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HOW TO CITE:

Michael Wilber,Iljung Kwak,Serge Belongie Cost-Effective HITs for Relative Similarity Comparisons Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (2014) 227-233.

Michael Wilber,Iljung Kwak,Serge Belongie Cost-Effective HITs for Relative Similarity Comparisons HCOMP 2014, 227-233.

Michael Wilber,Iljung Kwak,Serge Belongie (2014). Cost-Effective HITs for Relative Similarity Comparisons. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 227-233.

Michael Wilber,Iljung Kwak,Serge Belongie. Cost-Effective HITs for Relative Similarity Comparisons. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 2014 p.227-233.

Michael Wilber,Iljung Kwak,Serge Belongie. 2014. Cost-Effective HITs for Relative Similarity Comparisons. "Proceedings of the AAAI Conference on Human Computation and Crowdsourcing". 227-233.

Michael Wilber,Iljung Kwak,Serge Belongie. (2014) "Cost-Effective HITs for Relative Similarity Comparisons", Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, p.227-233

Michael Wilber,Iljung Kwak,Serge Belongie, "Cost-Effective HITs for Relative Similarity Comparisons", HCOMP, p.227-233, 2014.

Michael Wilber,Iljung Kwak,Serge Belongie. "Cost-Effective HITs for Relative Similarity Comparisons". Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2014, p.227-233.

Michael Wilber,Iljung Kwak,Serge Belongie. "Cost-Effective HITs for Relative Similarity Comparisons". Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, (2014): 227-233.

Michael Wilber,Iljung Kwak,Serge Belongie. Cost-Effective HITs for Relative Similarity Comparisons. HCOMP[Internet]. 2014[cited 2023]; 227-233.


ISSN: 2769-1349


Published by AAAI Press, Palo Alto, California USA
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Artificial Intelligence 1900 Embarcadero Road, Suite
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