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