  R-CNN style methods are sorts of the state-of-the-art object detection
methods, which consist of region proposal generation and deep CNN
classification. However, the proposal generation phase in this paradigm is
usually time consuming, which would slow down the whole detection time in
testing. This paper suggests that the value discrepancies among features in
deep convolutional feature maps contain plenty of useful spatial information,
and proposes a simple approach to extract the information for fast region
proposal generation in testing. The proposed method, namely Relief R-CNN
(R2-CNN), adopts a novel region proposal generator in a trained R-CNN style
model. The new generator directly generates proposals from convolutional
features by some simple rules, thus resulting in a much faster proposal
generation speed and a lower demand of computation resources. Empirical studies
show that R2-CNN could achieve the fastest detection speed with comparable
accuracy among all the compared algorithms in testing.
