  In recent years, convolutional neural networks (CNNs) with channel-wise
feature refining mechanisms have brought noticeable benefits to modelling
channel dependencies. However, current attention paradigms fail to infer an
optimal channel descriptor capable of simultaneously exploiting statistical and
spatial relationships among feature maps. In this paper, to overcome this
shortcoming, we present a novel channel-wise spatially autocorrelated (CSA)
attention mechanism. Inspired by geographical analysis, the proposed CSA
exploits the spatial relationships between channels of feature maps to produce
an effective channel descriptor. To the best of our knowledge, this is the f
irst time that the concept of geographical spatial analysis is utilized in deep
CNNs. The proposed CSA imposes negligible learning parameters and light
computational overhead to the deep model, making it a powerful yet efficient
attention module of choice. We validate the effectiveness of the proposed CSA
networks (CSA-Nets) through extensive experiments and analysis on ImageNet, and
MS COCO benchmark datasets for image classification, object detection, and
instance segmentation. The experimental results demonstrate that CSA-Nets are
able to consistently achieve competitive performance and superior
generalization than several state-of-the-art attention-based CNNs over
different benchmark tasks and datasets.
