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We propose a new zero-shot Event Detection method by Multi-modalDistributional Semantic embedding of videos. Our model embeds object and actionconcepts as well as other available modalities from videos into adistributional semantic space. To our knowledge, this is the first Zero-Shotevent detection model that is built on top of distributional semantics andextends it in the following directions: (a) semantic embedding of multimodalinformation in videos (with focus on the visual modalities), (b) automaticallydetermining relevance of concepts/attributes to a free text query, which couldbe useful for other applications, and (c) retrieving videos by free text eventquery (e.g., "changing a vehicle tire") based on their content. We embed videosinto a distributional semantic space and then measure the similarity betweenvideos and the event query in a free text form. We validated our method on thelarge TRECVID MED (Multimedia Event Detection) challenge. Using only the eventtitle as a query, our method outperformed the state-of-the-art that uses bigdescriptions from 12.6% to 13.5% with MAP metric and 0.73 to 0.83 with ROC-AUCmetric. It is also an order of magnitude faster.

Zero-Shot Event Detection by Multimodal Distributional Semantic Embedding of Videos