  This work presents a novel module, namely multi-branch concat (MBC), to
process the input tensor and obtain the multi-scale feature map. The proposed
MBC module brings new degrees of freedom (DoF) for the design of attention
networks by allowing the type of transformation operators and the number of
branches to be flexibly adjusted. Two important transformation operators,
multiplex and split, are considered in this work, both of which can represent
multi-scale features at a more granular level and increase the range of
receptive fields. By integrating the MBC and attention module, a multi-branch
attention (MBA) module is consequently developed to capture the channel-wise
interaction of feature maps for establishing the long-range channel dependency.
By substituting the 3x3 convolutions in the bottleneck blocks of the ResNet
with the proposed MBA, a novel block namely efficient multi-branch attention
(EMBA) is obtained, which can be easily plugged into the state-of-the-art
backbone CNN models. Furthermore, a new backbone network called EMBANet is
established by stacking the EMBA blocks. The proposed EMBANet is extensively
evaluated on representative computer vision tasks including: classification,
detection, and segmentation. And it demonstrates consistently superior
performance over the popular backbones.
