Referring Image Segmentation (RIS) aims to connect image and language via
outputting the corresponding object masks given a text description, which is a
fundamental vision-language task. Despite lots of works that have achieved
considerable progress for RIS, in this work, we explore an essential question,
"what if the description is wrong or misleading of the text description?". We
term such a sentence as a negative sentence. However, we find that existing
works cannot handle such settings. To this end, we propose a novel formulation
of RIS, named Robust Referring Image Segmentation (R-RIS). It considers the
negative sentence inputs besides the regularly given text inputs. We present
three different datasets via augmenting the input negative sentences and a new
metric to unify both input types. Furthermore, we design a new
transformer-based model named RefSegformer, where we introduce a token-based
vision and language fusion module. Such module can be easily extended to our
R-RIS setting by adding extra blank tokens. Our proposed RefSegformer achieves
the new state-of-the-art results on three regular RIS datasets and three R-RIS
datasets, which serves as a new solid baseline for further research. The
project page is at \url{https://lxtgh.github.io/project/robust_ref_seg/}.Comment: technical repor