  Channel and spatial attention mechanism has proven to provide an evident
performance boost of deep convolution neural networks (CNNs). Most existing
methods focus on one or run them parallel (series), neglecting the
collaboration between the two attentions. In order to better establish the
feature interaction between the two types of attention, we propose a
plug-and-play attention module, which we term "CAT"-activating the
Collaboration between spatial and channel Attentions based on learned Traits.
Specifically, we represent traits as trainable coefficients (i.e.,
colla-factors) to adaptively combine contributions of different attention
modules to fit different image hierarchies and tasks better. Moreover, we
propose the global entropy pooling (GEP) apart from global average pooling
(GAP) and global maximum pooling (GMP) operators, an effective component in
suppressing noise signals by measuring the information disorder of feature
maps. We introduce a three-way pooling operation into attention modules and
apply the adaptive mechanism to fuse their outcomes. Extensive experiments on
MS COCO, Pascal-VOC, Cifar-100, and ImageNet show that our CAT outperforms
existing state-of-the-art attention mechanisms in object detection, instance
segmentation, and image classification. The model and code will be released
soon.
