Efficient Semantic Segmentation with SERNet-Former

The author proposes SERNet-Former, an encoder-decoder architecture with Efficient-ResNet, AbGs, and AfNs to improve semantic segmentation efficiency by fusing global and local context information. The main thesis is that by integrating attention-boosting gates and fusion networks into the network architecture, significant improvements in semantic segmentation performance can be achieved.