  Convolutional Neural Networks (CNNs) have achieved tremendous success in a
number of learning tasks including image classification. Recent advanced models
in CNNs, such as ResNets, mainly focus on the skip connection to avoid gradient
vanishing. DenseNet designs suggest creating additional bypasses to transfer
features as an alternative strategy in network design. In this paper, we design
Attentive Feature Integration (AFI) modules, which are widely applicable to
most recent network architectures, leading to new architectures named AFI-Nets.
AFI-Nets explicitly model the correlations among different levels of features
and selectively transfer features with a little overhead.AFI-ResNet-152 obtains
a 1.24% relative improvement on the ImageNet dataset while decreases the FLOPs
by about 10% and the number of parameters by about 9.2% compared to ResNet-152.
