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In this work, two novel active learning approaches for the annotation and detection of audiovisual events are proposed. The assumption behind the proposed approaches is that events are susceptible to substantively deviate from the distribution of normal observations and therefore should be lying in regions of low density. Thus, it is believed that an event detection model can be trained more efficiently...
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