SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks

Improving the efficiency of state-of-the-art methods in semantic segmentation requires overcoming the increasing computational cost as well as issues such as fusing semantic information from global and local contexts. Based on the recent success and problems that convolutional neural networks (CNNs) encounter in semantic segmentation, this research proposes an encoder-decoder architecture with a unique efficient residual network. Attention-boosting gates (AbGs) and attention-boosting modules (AbMs) are deployed by aiming to fuse the feature-based semantic information with the global context of the …

art attention boosting cnns computational convolutional neural networks cost cs.ai cs.cv efficiency fusion gates global information network networks neural networks research residual segmentation semantic state success