High-order paired-ASPP for deep semantic segmentation networks.

Current semantic segmentation models only exploit first-order information, while rarely exploring high-order semantics. However, traditional first-order statistics are insufficient to support a solid unanimous representation. In this paper, we propose High-Order Paired-ASPP (Atrous Spatial Pyramid Pooling) Network to exploit high-order statistics from various feature levels. The network first introduces a High-Order Representation module to extract the contextual high-order information from all stages of the backbone. They can provide more semantic clues and discriminative information than the first-order ones. Besides, a Paired-ASPP module is proposed to embed high-order statistics of the early stages into the last stage. It can further preserve the boundary-related and spatial context in the low-level features for final prediction. Our experiments show that the high-order semantics significantly boost the performance on confusing objects. Our method achieves competitive performance without bells and whistles on three benchmarks, i.e., Cityscapes, ADE20K and Pascal-Context.