  We propose a new framework that automatically generates high-quality
segmentation masks with their referring expressions as pseudo supervisions for
referring image segmentation (RIS). These pseudo supervisions allow the
training of any supervised RIS methods without the cost of manual labeling. To
achieve this, we incorporate existing segmentation and image captioning
foundation models, leveraging their broad generalization capabilities. However,
the naive incorporation of these models may generate non-distinctive
expressions that do not distinctively refer to the target masks. To address
this challenge, we propose two-fold strategies that generate distinctive
captions: 1) 'distinctive caption sampling', a new decoding method for the
captioning model, to generate multiple expression candidates with detailed
words focusing on the target. 2) 'distinctiveness-based text filtering' to
further validate the candidates and filter out those with a low level of
distinctiveness. These two strategies ensure that the generated text
supervisions can distinguish the target from other objects, making them
appropriate for the RIS annotations. Our method significantly outperforms both
weakly and zero-shot SoTA methods on the RIS benchmark datasets. It also
surpasses fully supervised methods in unseen domains, proving its capability to
tackle the open-world challenge within RIS. Furthermore, integrating our method
with human annotations yields further improvements, highlighting its potential
in semi-supervised learning applications.
