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    <abstract>Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (VG), as annotators must write descriptions that distinguish target regions from visually similar ones. We tackle this by formulating active learning (AL) for VG under the realistic setting where only raw images are available without accompanying text. Since ground-truth text is unavailable, sample selection must estimate which images contain ambiguous regions that would require discriminative referring expressions. To address this, we generate auxiliary region-text pairs using foundation models, and introduce Referred Region Ambiguity, a new acquisition function that measures whether the model’s confidence collapses onto a single region or disperses across multiple candidates. It allows our method to prioritize images with strong cross-region competition, which are more informative due to their visual ambiguity. We also design a referring-expression annotation interface that helps annotators quickly focus on writing discriminative language with a few clicks. Experiments on RIS and REC benchmarks show that our AL framework consistently outperforms several AL baselines, while a user study shows up to 1.6\times faster description labeling of ours.</abstract>
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%0 Conference Proceedings
%T Cost-efficient Active Learning for Referring Image Segmentation and Grounding
%A Hong, Junbeom
%A Yu, Seonghoon
%A Jung, Hyung Rok
%A Kim, Sundong
%A Son, Jeany
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F hong-etal-2026-cost
%X Collecting natural-language referring expressions along with region annotations, such as masks or boxes, is a major bottleneck in visual grounding (VG), as annotators must write descriptions that distinguish target regions from visually similar ones. We tackle this by formulating active learning (AL) for VG under the realistic setting where only raw images are available without accompanying text. Since ground-truth text is unavailable, sample selection must estimate which images contain ambiguous regions that would require discriminative referring expressions. To address this, we generate auxiliary region-text pairs using foundation models, and introduce Referred Region Ambiguity, a new acquisition function that measures whether the model’s confidence collapses onto a single region or disperses across multiple candidates. It allows our method to prioritize images with strong cross-region competition, which are more informative due to their visual ambiguity. We also design a referring-expression annotation interface that helps annotators quickly focus on writing discriminative language with a few clicks. Experiments on RIS and REC benchmarks show that our AL framework consistently outperforms several AL baselines, while a user study shows up to 1.6\times faster description labeling of ours.
%R 10.18653/2026.findings-emnlp.1635
%U https://aclanthology.org/2026.findings-emnlp.1635/
%U https://doi.org/10.18653/2026.findings-emnlp.1635
%P 34765-34788

Markdown (Informal)

[Cost-efficient Active Learning for Referring Image Segmentation and Grounding](https://aclanthology.org/2026.findings-emnlp.1635/) (Hong et al., Findings 2026)

• Cost-efficient Active Learning for Referring Image Segmentation and Grounding (Hong et al., Findings 2026)

ACL

• Junbeom Hong, Seonghoon Yu, Hyung Rok Jung, Sundong Kim, and Jeany Son. 2026. Cost-efficient Active Learning for Referring Image Segmentation and Grounding. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 34765–34788, Budapest, Hungary. Association for Computational Linguistics.