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Curriculum Point Prompting for Weakly-Supervised Referring Image Segmentation
April 19, 2024, 4:44 a.m. | Qiyuan Dai, Sibei Yang
cs.CV updates on arXiv.org arxiv.org
Abstract: Referring image segmentation (RIS) aims to precisely segment referents in images through corresponding natural language expressions, yet relying on cost-intensive mask annotations. Weakly supervised RIS thus learns from image-text pairs to pixel-level semantics, which is challenging for segmenting fine-grained masks. A natural approach to enhancing segmentation precision is to empower weakly supervised RIS with the image segmentation foundation model SAM. Nevertheless, we observe that simply integrating SAM yields limited benefits and can even lead to …
abstract annotations arxiv cost cs.cv curriculum fine-grained image images language masks natural natural language pixel precision prompting segment segmentation semantics text through type weakly-supervised
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