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Referring image segmentation, the task of segmenting any arbitrary entitiesdescribed in free-form texts, opens up a variety of vision applications.However, manual labeling of training data for this task is prohibitivelycostly, leading to lack of labeled data for training. We address this issue bya weakly supervised learning approach using text descriptions of trainingimages as the only source of supervision. To this end, we first present a newmodel that discovers semantic entities in input image and then combines suchentities relevant to text query to predict the mask of the referent. We alsopresent a new loss function that allows the model to be trained without anyfurther supervision. Our method was evaluated on four public benchmarks forreferring image segmentation, where it clearly outperformed the existing methodfor the same task and recent open-vocabulary segmentation models on all thebenchmarks.

Shatter and Gather: Learning Referring Image Segmentation with Text Supervision