Referring expression segmentation (RES), a task that involves localizing
specific instance-level objects based on free-form linguistic descriptions, has
emerged as a crucial frontier in human-AI interaction. It demands an intricate
understanding of both visual and textual contexts and often requires extensive
training data. This paper introduces RESMatch, the first semi-supervised
learning (SSL) approach for RES, aimed at reducing reliance on exhaustive data
annotation. Extensive validation on multiple RES datasets demonstrates that
RESMatch significantly outperforms baseline approaches, establishing a new
state-of-the-art. Although existing SSL techniques are effective in image
segmentation, we find that they fall short in RES. Facing the challenges
including the comprehension of free-form linguistic descriptions and the
variability in object attributes, RESMatch introduces a trifecta of
adaptations: revised strong perturbation, text augmentation, and adjustments
for pseudo-label quality and strong-weak supervision. This pioneering work lays
the groundwork for future research in semi-supervised learning for referring
expression segmentation.

该研究介绍了 RESMatch，这是一种用于参考表达分割的半监督学习方法，旨在减少对详尽数据标注的依赖，通过修订强扰动、文本增强和强弱监督质量调整等三种创新，RESMatch 在多个数据集上显著优于基线方法，并树立了新的最优效果，为以后的半监督学习参考表达分割研究奠定了基础。