  Referring Image Segmentation (RIS) aims at segmenting the target object from
an image referred by one given natural language expression. The diverse and
flexible expressions as well as complex visual contents in the images raise the
RIS model with higher demands for investigating fine-grained matching behaviors
between words in expressions and objects presented in images. However, such
matching behaviors are hard to be learned and captured when the visual cues of
referents (i.e. referred objects) are insufficient, as the referents with weak
visual cues tend to be easily confused by cluttered background at boundary or
even overwhelmed by salient objects in the image. And the insufficient visual
cues issue can not be handled by the cross-modal fusion mechanisms as done in
previous work. In this paper, we tackle this problem from a novel perspective
of enhancing the visual information for the referents by devising a Two-stage
Visual cues enhancement Network (TV-Net), where a novel Retrieval and
Enrichment Scheme (RES) and an Adaptive Multi-resolution feature Fusion (AMF)
module are proposed. Through the two-stage enhancement, our proposed TV-Net
enjoys better performances in learning fine-grained matching behaviors between
the natural language expression and image, especially when the visual
information of the referent is inadequate, thus produces better segmentation
results. Extensive experiments are conducted to validate the effectiveness of
the proposed method on the RIS task, with our proposed TV-Net surpassing the
state-of-the-art approaches on four benchmark datasets.
