Cost-Effective HITs for Relative Similarity Comparisons
This work addresses the cost and efficiency challenges in collecting triplet data for computer vision and machine learning applications, offering incremental improvements in crowdsourcing practices.
The paper tackled the problem of efficiently collecting relative similarity comparisons (triplets) for embeddings by exploring alternative user interface displays for crowdsourcing tasks, resulting in higher quality embeddings through simple UI changes rather than altering sampling algorithms.
Similarity comparisons of the form "Is object a more similar to b than to c?" are useful for computer vision and machine learning applications. Unfortunately, an embedding of $n$ points is specified by $n^3$ triplets, making collecting every triplet an expensive task. In noticing this difficulty, other researchers have 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, it is also important to understand how best to display a triplet collection task to a user. In this work we explore an alternative display for collecting triplets and analyze the monetary cost and speed of the display. 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 lead to much higher quality embeddings. We also provide a dataset as well as the labels collected from crowd workers.