Boosting semantic segmentation via feature enhancement

Semantic segmentation aims to map each pixel of an image into its corresponding semantic label. Most existing methods either mainly concentrate on high-level features or simple combination of low-level and high-level features from backbone convolutional networks, which may weaken or even ignore the compensation between different levels. To effectively take advantages from both shallow (textural) and deep (semantic) features, this paper proposes a novel plug-and-play module, namely feature enhancement module (FEM). The proposed FEM first uses an information extractor to extract the desired details or semantics from different stages, and then enhances target features by taking in the extracted message. Two types of FEM, i.e., detail FEM and semantic FEM, can be customized. Concretely, the former type strengthens textural information to protect key but tiny/low-contrast details from suppression/removal, while the other one highlights structural information to boost segmentation performance. By equipping a given backbone network with FEMs, there might contain two information flows, i.e., detail flow and semantic flow. Extensive experiments on the Cityscapes, ADE20K and PASCAL Context datasets are conducted to validate the effectiveness of our design. The code has been released at https://github.com/SuperZ-Liu/FENet.