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LADNet: an ultra-lightweight and efficient Dilated Residual Network with Light-Attention Module
2021
IEEE Access
Image classification task is an important branch of computer vision. At present, most of the mainstream CNNs are large in size and take up too much computing resources. The quality-price ratio is not satisfying when the heavy CNNs are used in image classification. So, this work proposes a spatial and channel hybrid attention module (Light-Attention module), an ultra-lightweight but efficient attention module. Given an intermediate feature map, the Light-Attention module will firstly derive the
doi:10.1109/access.2021.3065338
fatcat:nqqjgzirfrcuzftgphimzqft64