SelfLearned Feature Reconstruction and OffsetDilated Feature Fusion for RealTime Semantic Segmentation Recent approaches for realtime semantic segmentation usually employ the encoderdecoder architecture as the backbone to generate a highquality segmentation prediction. There has been a lot of research on designing efficient encoding methods. However, enhancing the performance of components in decoder is also crucial for pixellevel recognition. In this paper, we propose a selflearned feature reconstruction (SFR) method and an offsetdilated feature fusion (ODFF) module to improve the prediction reconstruction capability of the decoder. Concretely, SFR can effectively reconstruct the highresolution feature maps by recombining feature space, in which the space transformation matrix implicitly contained in a convolution layer can selectively highlight features at each position by leveraging the knowledge of label space in a selflearned way. Moreover, ODFF module can effectively fuse multilevel features with multiscale contextual information by feeding the feature maps into designed parallel offsetdilated convolutions, which enhances the feature representation capability of the decoder. Experiments on Cityscapes and CamVid datasets demonstrate the superior performance of our proposed methods embedded in ESPNet.

Preserving details in semanticsaware context for scene parsing Great success of scene parsing (also known as, semantic segmentation) has been achieved with the pipeline of fully convolutional networks (FCNs). Nevertheless, there are a lot of segmentation failures caused by large similarities between local appearances. To alleviate the problem, most of existing methods attempt to improve the global view of FCNs by introducing different contextual modules. Though the reconstructed high resolution output of these methods is of rich semantics, it cannot faithfully recover the fine image details owing to lack of desired precise lowlevel information. To overcome the problem, we propose to improve the spatial decoding process through embedding possibly lost lowlevel information in a principled way. To this end, we make the following three contributions. First, we propose a semantics conformity module to make lowlevel features variations agnostic. Second, we introduce semantics into the conformed low level features through guidance from semantically aware features. Finally, we institute the availability of various possible contextual features at feature fusion to enrich context information. The proposed approach demonstrates competitive performance on challenging PASCAL VOC 2012, Cityscapes, and ADE20K benchmarks in comparison to the stateoftheart methods.