1). Lack of speed analysis, the experiments have compared GFLOPs of different segmentation networks. However, there is no comparisons of inference speed between the proposed network and prior work. The improvement on inference speed will be more interesting than reducing FLOPs. 2). For the detail of the proposed NRD, it is reasonable that the guidance maps are generated from the low-level feature maps. And the guidance maps can be predicted from the the first-stage feature maps or the second-stage feature maps. It is better to provide one ablation study about the effect for them. 3). Important references are missing. The GFF[1] and EfficientFCN[2] both aims to implement the fast semantic segmentation method in the encode-decoder architecture. I encourage the authors to have a comprehensive comparison with these work.
[1]. Gated Fully Fusion for Semantic Segmentation, AAAI'20. [2]. EfficientFCN: Holistically-guided Decoding for Semantic Segmentation, ECCV'20.
See above. The societal impact is shown one the last page of the manuscript.
1). Lack of speed analysis, the experiments have compared GFLOPs of different segmentation networks. However, there is no comparisons of inference speed between the proposed network and prior work. The improvement on inference speed will be more interesting than reducing FLOPs.