  An increasing number of computer vision tasks can be tackled with deep
features, which are the intermediate outputs of a pre-trained Convolutional
Neural Network. Despite the astonishing performance, deep features extracted
from low-level neurons are still below satisfaction, arguably because they
cannot access the spatial context contained in the higher layers. In this
paper, we present InterActive, a novel algorithm which computes the activeness
of neurons and network connections. Activeness is propagated through a neural
network in a top-down manner, carrying high-level context and improving the
descriptive power of low-level and mid-level neurons. Visualization indicates
that neuron activeness can be interpreted as spatial-weighted neuron responses.
We achieve state-of-the-art classification performance on a wide range of image
datasets.
