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Event-specific concepts are the semantic concepts designed for the events ofinterest, which can be used as a mid-level representation of complex events invideos. Existing methods only focus on defining event-specific concepts for asmall number of predefined events, but cannot handle novel unseen events. Thismotivates us to build a large scale event-specific concept library that coversas many real-world events and their concepts as possible. Specifically, wechoose WikiHow, an online forum containing a large number of how-to articles onhuman daily life events. We perform a coarse-to-fine event discovery processand discover 500 events from WikiHow articles. Then we use each event name asquery to search YouTube and discover event-specific concepts from the tags ofreturned videos. After an automatic filter process, we end up with 95,321videos and 4,490 concepts. We train a Convolutional Neural Network (CNN) modelon the 95,321 videos over the 500 events, and use the model to extract deeplearning feature from video content. With the learned deep learning feature, wetrain 4,490 binary SVM classifiers as the event-specific concept library. Theconcepts and events are further organized in a hierarchical structure definedby WikiHow, and the resultant concept library is called EventNet. Finally, theEventNet concept library is used to generate concept based representation ofevent videos. To the best of our knowledge, EventNet represents the first videoevent ontology that organizes events and their concepts into a semanticstructure. It offers great potential for event retrieval and browsing.Extensive experiments over the zero-shot event retrieval task when no trainingsamples are available show that the EventNet concept library consistently andsignificantly outperforms the state-of-the-art (such as the 20K ImageNetconcepts trained with CNN) by a large margin up to 207%.

EventNet: A Large Scale Structured Concept Library for Complex Event Detection in Video