  Context information plays an indispensable role in the success of semantic
segmentation. Recently, non-local self-attention based methods are proved to be
effective for context information collection. Since the desired context
consists of spatial-wise and channel-wise attentions, 3D representation is an
appropriate formulation. However, these non-local methods describe 3D context
information based on a 2D similarity matrix, where space compression may lead
to channel-wise attention missing. An alternative is to model the contextual
information directly without compression. However, this effort confronts a
fundamental difficulty, namely the high-rank property of context information.
In this paper, we propose a new approach to model the 3D context
representations, which not only avoids the space compression but also tackles
the high-rank difficulty. Here, inspired by tensor canonical-polyadic
decomposition theory (i.e, a high-rank tensor can be expressed as a combination
of rank-1 tensors.), we design a low-rank-to-high-rank context reconstruction
framework (i.e, RecoNet). Specifically, we first introduce the tensor
generation module (TGM), which generates a number of rank-1 tensors to capture
fragments of context feature. Then we use these rank-1 tensors to recover the
high-rank context features through our proposed tensor reconstruction module
(TRM). Extensive experiments show that our method achieves state-of-the-art on
various public datasets. Additionally, our proposed method has more than 100
times less computational cost compared with conventional non-local-based
methods.
