underlies standard statistical accounts of “wisdom-of-crowds” phenomena (23). Furthermore, while it is commonly assumed that groups of individuals are correct in mean expectation (22, 24), humans’ independent estimates can be systematically biased (25, 26). . In this paper, we propose to study the common ex-pression perception by a reliable crowdsourcing approach. Specifically, our well-trained annotators are asked to label face images with one of the seven basic categories [11], and each face is annotated enough times independently, i.e. about 40 times in our experiment. Then, the noisy labels. All groups and messages.
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underlies standard statistical accounts of “wisdom-of-crowds” phenomena (23). Furthermore, while it is commonly assumed that groups of individuals are correct in mean expectation (22, 24), humans’ independent estimates can be systematically biased (25, 26). However, although these independence and collective. Human crowds are ubiquitous in the real world, making their simu-lation a necessity for realistic interactive environments. Physically correct crowd models also have applications outside of computer graphics in psychology, transportation research, and architecture. In this paper we focus on real-time synthesis of crowd motion for. Crowds. They can be controlled to play a significant narrative role in an authored story, or individual members can be removed from the crowd at will to be featured as more principal characters with no loss in fidelity. Contributions. This paper contributes an authoring tool with broad freedom in designing narratives that take place in.
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