  Most automatic matting methods try to separate the salient foreground from
the background. However, the insufficient quantity and subjective bias of the
current existing matting datasets make it difficult to fully explore the
semantic association between object-to-object and object-to-environment in a
given image. In this paper, we propose a Situational Perception Guided Image
Matting (SPG-IM) method that mitigates subjective bias of matting annotations
and captures sufficient situational perception information for better global
saliency distilled from the visual-to-textual task. SPG-IM can better associate
inter-objects and object-to-environment saliency, and compensate the subjective
nature of image matting and its expensive annotation. We also introduce a
textual Semantic Transformation (TST) module that can effectively transform and
integrate the semantic feature stream to guide the visual representations. In
addition, an Adaptive Focal Transformation (AFT) Refinement Network is proposed
to adaptively switch multi-scale receptive fields and focal points to enhance
both global and local details. Extensive experiments demonstrate the
effectiveness of situational perception guidance from the visual-to-textual
tasks on image matting, and our model outperforms the state-of-the-art methods.
We also analyze the significance of different components in our model. The code
will be released soon.
