Title: DMSANET: DUAL MULTI SCALE ATTENTION NETWORK. Abstract: Attention mechanism of late has been quite popular in the computer vision community. A lot of work has been done to improve the performance of the network,
although almost always it results in increased computational complexity. In this paper, we propose a new attention module that not only achieves the best performance
but also has lesser parameters compared to most existing models. Our attention
module can easily be integrated with other convolutional neural networks because
of its lightweight nature. The proposed network named Dual Multi Scale Attention
Network (DMSANet) is comprised of two parts: the first part is used to extract
features at various scales and aggregate them, the second part uses spatial and
channel attention modules in parallel to adaptively integrate local features with
their global dependencies. We benchmark our network performance for Image
Classification on ImageNet dataset, Object Detection and Instance Segmentation
both on MS COCO dataset.