SPGNet: Semantic Prediction Guidance for Scene Parsing
Multi scale context module and single stage encoder decoder structure are commonly employed for semantic segmentation. The multi scale context module refers to the operations to aggregate feature responses from a large spatial extent, while the single stage encoder decoder structure encodes the high level semantic information in the encoder path and recovers the boundary information in the decoder path. In contrast, multi stage encoder decoder networks have been widely used in human pose estimation and show superior performance than their single stage counterpart. However, few efforts have been attempted to bring this effective design to semantic segmentation. In this work, we propose a Semantic Prediction Guidance (SPG) module which learns to re weight the local features through the guidance from pixel wise semantic prediction. We find that by carefully re weighting features across stages, a two stage encoder decoder network coupled with our proposed SPG module can significantly outperform its one stage counterpart with similar parameters and computations. Finally, we report experimental results on the semantic segmentation benchmark Cityscapes, in which our SPGNet attains 81.1% on the test set using only 'fine' annotations.
2019 IEEE/CVF International Conference on Computer Vision (ICCV)