  The millions of movies produced in the human history are valuable resources
for computer vision research. However, learning a vision model from movie data
would meet with serious difficulties. A major obstacle is the computational
cost -- the length of a movie is often over one hour, which is substantially
longer than the short video clips that previous study mostly focuses on. In
this paper, we explore an alternative approach to learning vision models from
movies. Specifically, we consider a framework comprised of a visual module and
a temporal analysis module. Unlike conventional learning methods, the proposed
approach learns these modules from different sets of data -- the former from
trailers while the latter from movies. This allows distinctive visual features
to be learned within a reasonable budget while still preserving long-term
temporal structures across an entire movie. We construct a large-scale dataset
for this study and define a series of tasks on top. Experiments on this dataset
showed that the proposed method can substantially reduce the training time
while obtaining highly effective features and coherent temporal structures.
