  Event recognition from still images is one of the most important problems for
image understanding. However, compared with object recognition and scene
recognition, event recognition has received much less research attention in
computer vision community. This paper addresses the problem of cultural event
recognition in still images and focuses on applying deep learning methods on
this problem. In particular, we utilize the successful architecture of
Object-Scene Convolutional Neural Networks (OS-CNNs) to perform event
recognition. OS-CNNs are composed of object nets and scene nets, which transfer
the learned representations from the pre-trained models on large-scale object
and scene recognition datasets, respectively. We propose four types of
scenarios to explore OS-CNNs for event recognition by treating them as either
"end-to-end event predictors" or "generic feature extractors". Our experimental
results demonstrate that the global and local representations of OS-CNNs are
complementary to each other. Finally, based on our investigation of OS-CNNs, we
come up with a solution for the cultural event recognition track at the ICCV
ChaLearn Looking at People (LAP) challenge 2015. Our team secures the third
place at this challenge and our result is very close to the best performance.
