Distance Guided Channel Weighting for Semantic Segmentation

Recent works have achieved great success in improving the performance of multiple computer vision tasks by capturing features with a high channel number utilizing deep neural networks. However, many channels of extracted features are not discriminative and contain a lot of redundant information. In this paper, we address above issue by introducing the Distance Guided Channel Weighting (DGCW) Module. The DGCW module is constructed in a pixel-wise context extraction manner, which enhances the discriminativeness of features by weighting different channels of each pixel's feature vector when modeling its relationship with other pixels. It can make full use of the high-discriminative information while ignore the low-discriminative information containing in feature maps, as well as capture the long-range dependencies. Furthermore, by incorporating the DGCW module with a baseline segmentation network, we propose the Distance Guided Channel Weighting Network (DGCWNet). We conduct extensive experiments to demonstrate the effectiveness of DGCWNet. In particular, it achieves 81.6% mIoU on Cityscapes with only fine annotated data for training, and also gains satisfactory performance on another two semantic segmentation datasets, i.e. Pascal Context and ADE20K. Code will be available soon at https://github.com/LanyunZhu/DGCWNet

Approaching Neural Chinese Word Segmentation as a Low-Resource Machine Translation Task

Supervised Chinese word segmentation has entered the deep learning era which reduces the hassle of feature engineering. Recently, some researchers attempted to treat it as character-level translation which further simplified model designing and building, but there is still a performance gap between the translation-based approach and other methods. In this work, we apply the best practices from low-resource neural machine translation to Chinese word segmentation. We build encoder-decoder models with attention, and examine a series of techniques including regularization, data augmentation, objective weighting, transfer learning and ensembling. Our method is generic for word segmentation, without the need for feature engineering or model implementation. In the closed test with constrained data, our method ties with the state of the art on the MSR dataset and is comparable to other methods on the PKU dataset