Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
VizWiz: nearly real-time answers to visual questions
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Crowds in two seconds: enabling realtime crowd-powered interfaces
Proceedings of the 24th annual ACM symposium on User interface software and technology
Real-time crowd labeling for deployable activity recognition
Proceedings of the 2013 conference on Computer supported cooperative work
Cascade: crowdsourcing taxonomy creation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Training intelligent systems is a time-consuming and costly process that often limits real-world applications. Prior work has attempted to compensate for this challenge by generating sets of labeled training data for machine learning algorithms using affordable human contributors. In this paper, we present ARchitect, a system that uses the crowd to extract context-dependent relational structure. We focus on activity recognition because of its broad applicability, high level of variation, and difficulty of training systems a priority. We demonstrate that using our approach, the crowd can accurately and consistently identify relationships between actions even over sessions containing different workers and varied executions of an activity. This results in the ability to identify multiple valid execution paths from a single observation, suggesting that one-off learning can be facilitated by using the crowd as an on-demand source of human intelligence in the knowledge acquisition process.