  In this paper, we introduce SemiRES, a semi-supervised framework that
effectively leverages a combination of labeled and unlabeled data to perform
RES. A significant hurdle in applying semi-supervised techniques to RES is the
prevalence of noisy pseudo-labels, particularly at the boundaries of objects.
SemiRES incorporates the Segment Anything Model (SAM), renowned for its precise
boundary demarcation, to improve the accuracy of these pseudo-labels. Within
SemiRES, we offer two alternative matching strategies: IoU-based Optimal
Matching (IOM) and Composite Parts Integration (CPI). These strategies are
designed to extract the most accurate masks from SAM's output, thus guiding the
training of the student model with enhanced precision. In instances where a
precise mask cannot be matched from the available candidates, we develop the
Pixel-Wise Adjustment (PWA) strategy, guiding the student model's training
directly by the pseudo-labels. Extensive experiments on three RES
benchmarks--RefCOCO, RefCOCO+, and G-Ref reveal its superior performance
compared to fully supervised methods. Remarkably, with only 1% labeled data,
our SemiRES outperforms the supervised baseline by a large margin, e.g. +18.64%
gains on RefCOCO val set. The project code is available at
\url{https://github.com/nini0919/SemiRES}.
