  Referring image segmentation aims to segment an object referred to by natural
language expression from an image. However, this task is challenging due to the
distinct data properties between text and image, and the randomness introduced
by diverse objects and unrestricted language expression. Most of previous work
focus on improving cross-modal feature fusion while not fully addressing the
inherent uncertainty caused by diverse objects and unrestricted language. To
tackle these problems, we propose an end-to-end Multi-Mask Network for
referring image segmentation(MMNet). we first combine picture and language and
then employ an attention mechanism to generate multiple queries that represent
different aspects of the language expression. We then utilize these queries to
produce a series of corresponding segmentation masks, assigning a score to each
mask that reflects its importance. The final result is obtained through the
weighted sum of all masks, which greatly reduces the randomness of the language
expression. Our proposed framework demonstrates superior performance compared
to state-of-the-art approaches on the two most commonly used datasets, RefCOCO,
RefCOCO+ and G-Ref, without the need for any post-processing. This further
validates the efficacy of our proposed framework.
