  While recent deep neural networks have achieved a promising performance on
object recognition, they rely implicitly on the visual contents of the whole
image. In this paper, we train deep neural net- works on the foreground
(object) and background (context) regions of images respectively. Consider- ing
human recognition in the same situations, net- works trained on the pure
background without ob- jects achieves highly reasonable recognition performance
that beats humans by a large margin if only given context. However, humans
still outperform networks with pure object available, which indicates networks
and human beings have different mechanisms in understanding an image.
Furthermore, we straightforwardly combine multiple trained networks to explore
different visual cues learned by different networks. Experiments show that
useful visual hints can be explicitly learned separately and then combined to
achieve higher performance, which verifies the advantages of the proposed
framework.
