Referring Expression Segmentation (RES) aims to provide a segmentation mask of the target object in an image referred to by the text (i.e., referring expression). Existing methods require large-scale mask annotations. Moreover, such approaches do not generalize well to unseen/zero-shot scenarios. To address the aforementioned issues, we propose a weakly-supervised bootstrapping architecture for RES with several new algorithmic innovations. To the best of our knowledge, ours is the first approach that considers only a fraction of both mask and box annotations (shown in Figure 1 and Table 1) for training. To enable principled training of models in such low-annotation settings, improve image-text region-level alignment, and further enhance spatial localization of the target object in the image, we propose Cross-modal Fusion with Attention Consistency module. For automatic pseudo-labeling of unlabeled samples, we introduce a novel Mask Validity Filtering routine based on a spatially aware zero-shot proposal scoring approach. Extensive experiments show that with just 30% annotations, our model SafaRi achieves 59.31 and 48.26 mIoUs as compared to 58.93 and 48.19 mIoUs obtained by the fully-supervised SOTA method SeqTR respectively on RefCOCO+@testA and RefCOCO+testB datasets. SafaRi also outperforms SeqTR by 11.7% (on RefCOCO+testA) and 19.6% (on RefCOCO+testB) in a fully-supervised setting and demonstrates strong generalization capabilities in unseen/zero-shot tasks.

翻译：指称表达式分割（RES）旨在根据文本（即指称表达式）为图像中的目标对象提供分割掩码。现有方法需要大规模掩码标注，且此类方法在未见/零样本场景中泛化能力有限。针对上述问题，我们提出一种弱监督自举架构用于RES，并引入多项新算法创新。据我们所知，本研究首次提出仅使用部分掩码与边界框标注（如图1与表1所示）进行训练的方法。为在此低标注条件下实现模型的理论训练、提升图文区域级对齐能力并进一步增强图像中目标对象的空间定位精度，我们提出基于注意力一致性的跨模态融合模块。针对未标注样本的自动伪标签生成，我们引入一种基于空间感知零样本建议评分的新型掩码有效性过滤机制。大量实验表明：在仅使用30%标注数据时，我们的模型SafaRi在RefCOCO+@testA和RefCOCO+testB数据集上分别取得59.31和48.26的mIoU，优于全监督SOTA方法SeqTR的58.93和48.19 mIoU。在完全监督设定下，SafaRi在RefCOCO+testA和RefCOCO+testB上分别以11.7%和19.6%的优势超越SeqTR，并在未见/零样本任务中展现出强大的泛化能力。