  Referring Image Segmentation (RIS) consistently requires language and
appearance semantics to more understand each other. The need becomes acute
especially under hard situations. To achieve, existing works tend to resort to
various trans-representing mechanisms to directly feed forward language
semantic along main RGB branch, which however will result in referent
distribution weakly-mined in space and non-referent semantic contaminated along
channel. In this paper, we propose Spatial Semantic Recurrent Mining
(S\textsuperscript{2}RM) to achieve high-quality cross-modality fusion. It
follows a working strategy of trilogy: distributing language feature, spatial
semantic recurrent coparsing, and parsed-semantic balancing. During fusion,
S\textsuperscript{2}RM will first generate a constraint-weak yet
distribution-aware language feature, then bundle features of each row and
column from rotated features of one modality context to recurrently correlate
relevant semantic contained in feature from other modality context, and finally
resort to self-distilled weights to weigh on the contributions of different
parsed semantics. Via coparsing, S\textsuperscript{2}RM transports information
from the near and remote slice layers of generator context to the current slice
layer of parsed context, capable of better modeling global relationship
bidirectional and structured. Besides, we also propose a Cross-scale Abstract
Semantic Guided Decoder (CASG) to emphasize the foreground of the referent,
finally integrating different grained features at a comparatively low cost.
Extensive experimental results on four current challenging datasets show that
our proposed method performs favorably against other state-of-the-art
algorithms.
