Abstract

The power of crowds, leveraging a large number of human contributors and the capabilities of human computation, has enormous potential to address key challenges in the area of multimedia research. This power is, however, of difficult exploitation: challenges arise from the fact that a community of users or workers is a complex and dynamic system highly sensitive to changes in the form and the parameterization of their activities. Since 2012, the International ACM Workshop on Crowdsourcing for Multimedia \emph{CrowdMM} has been the venue for collecting new insights on the effective deployment of crowdsourcing towards boosting Multimedia research. In its third edition, CrowdMM14 especially focuses on contributions that propose solutions for the key challenges that face widespread adoption of crowdsourcing paradigms in the multimedia research community. These include: identification of optimal crowd members (e.g., user expertise, worker reliability), providing effective explanations (i.e., good task design), controlling noise and quality in the results, designing incentive structures that do not breed cheating, adversarial environments, gathering necessary background information about crowd members without violating privacy, controlling descriptions of task.

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