reviseFor the RBA-Net the backbone of encoder is ResNet-50 pre-trained on ImageNet The batch size is set to 8 and Adam optimizer is adopted for training network according to the end-to-end way The in

The RBA-Net utilizes ResNet-50 as the encoder backbone, which is pre-trained on ImageNet. The network is trained using the end-to-end approach, with a batch size of 8 and Adam optimizer. The initial learning rate is set to 0.0001, and a "poly" learning rate policy is implemented for all experiments. The learning rate is multiplied by MM after each iteration, and training is terminated after 100 epochs.