  Learning discriminative global features plays a vital role in semantic
segmentation. And most of the existing methods adopt stacks of local
convolutions or non-local blocks to capture long-range context. However, due to
the absence of spatial structure preservation, these operators ignore the
object details when enlarging receptive fields. In this paper, we propose the
learnable tree filter to form a generic tree filtering module that leverages
the structural property of minimal spanning tree to model long-range
dependencies while preserving the details. Furthermore, we propose a highly
efficient linear-time algorithm to reduce resource consumption. Thus, the
designed modules can be plugged into existing deep neural networks
conveniently. To this end, tree filtering modules are embedded to formulate a
unified framework for semantic segmentation. We conduct extensive ablation
studies to elaborate on the effectiveness and efficiency of the proposed
method. Specifically, it attains better performance with much less overhead
compared with the classic PSP block and Non-local operation under the same
backbone. Our approach is proved to achieve consistent improvements on several
benchmarks without bells-and-whistles. Code and models are available at
https://github.com/StevenGrove/TreeFilter-Torch.
