  Attentively important regions in video frames account for a majority part of
the semantics in each frame. This information is helpful in many applications
not only for entertainment (such as auto generating commentary and tourist
guide) but also for robotic control which holds a larascope supported for
laparoscopic surgery. However, it is not always straightforward to define and
locate such semantic regions in videos. In this work, we attempt to address the
problem of attending relevant regions in videos by leveraging the eye fixations
labels with a RNN-based visual attention model. Our experimental results
suggest that this approach holds a good potential to learn to attend semantic
regions in videos while its performance also heavily relies on the quality of
eye fixations labels.
