  Event recognition in still images is an intriguing problem and has potential
for real applications. This paper addresses the problem of event recognition by
proposing a convolutional neural network that exploits knowledge of objects and
scenes for event classification (OS2E-CNN). Intuitively, it stands to reason
that there exists a correlation among the concepts of objects, scenes, and
events. We empirically demonstrate that the recognition of objects and scenes
substantially contributes to the recognition of events. Meanwhile, we propose
an iterative selection method to identify a subset of object and scene classes,
which help to more efficiently and effectively transfer their deep
representations to event recognition. Specifically, we develop three types of
transferring techniques: (1) initialization-based transferring, (2)
knowledge-based transferring, and (3) data-based transferring. These newly
designed transferring techniques exploit multi-task learning frameworks to
incorporate extra knowledge from other networks and additional datasets into
the training procedure of event CNNs. These multi-task learning frameworks turn
out to be effective in reducing the effect of over-fitting and improving the
generalization ability of the learned CNNs. With OS2E-CNN, we design a
multi-ratio and multi-scale cropping strategy, and propose an end-to-end event
recognition pipeline. We perform experiments on three event recognition
benchmarks: the ChaLearn Cultural Event Recognition dataset, the Web Image
Dataset for Event Recognition (WIDER), and the UIUC Sports Event dataset. The
experimental results show that our proposed algorithm successfully adapts
object and scene representations towards the event dataset and that it achieves
the current state-of-the-art performance on these challenging datasets.
