  In this paper, we focus on automatically detecting events in unconstrained
videos without the use of any visual training exemplars. In principle,
zero-shot learning makes it possible to train an event detection model based on
the assumption that events (e.g. \emph{birthday party}) can be described by
multiple mid-level semantic concepts (e.g. "blowing candle", "birthday cake").
Towards this goal, we first pre-train a bundle of concept classifiers using
data from other sources. Then we evaluate the semantic correlation of each
concept \wrt the event of interest and pick up the relevant concept
classifiers, which are applied on all test videos to get multiple prediction
score vectors. While most existing systems combine the predictions of the
concept classifiers with fixed weights, we propose to learn the optimal weights
of the concept classifiers for each testing video by exploring a set of online
available videos with free-form text descriptions of their content. To validate
the effectiveness of the proposed approach, we have conducted extensive
experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset.
The experimental results confirm the superiority of the proposed approach.
