  Existing semantic segmentation works mainly focus on learning the contextual
information in high-level semantic features with CNNs. In order to maintain a
precise boundary, low-level texture features are directly skip-connected into
the deeper layers. Nevertheless, texture features are not only about local
structure, but also include global statistical knowledge of the input image. In
this paper, we fully take advantages of the low-level texture features and
propose a novel Statistical Texture Learning Network (STLNet) for semantic
segmentation. For the first time, STLNet analyzes the distribution of low level
information and efficiently utilizes them for the task. Specifically, a novel
Quantization and Counting Operator (QCO) is designed to describe the texture
information in a statistical manner. Based on QCO, two modules are introduced:
(1) Texture Enhance Module (TEM), to capture texture-related information and
enhance the texture details; (2) Pyramid Texture Feature Extraction Module
(PTFEM), to effectively extract the statistical texture features from multiple
scales. Through extensive experiments, we show that the proposed STLNet
achieves state-of-the-art performance on three semantic segmentation
benchmarks: Cityscapes, PASCAL Context and ADE20K.
