这个新方法能让视觉定位标注快1.6倍，特别适合需要大量标注数据的团队。
研究人员提出了一种针对视觉定位的主动学习方法，解决了标注瓶颈问题。该方法在没有配套文本的情况下，通过基础模型生成辅助区域-文本对，引入" referred region ambiguity"获取函数。在RIS和REC基准测试中，该方法优于多种基线，用户研究显示标注速度提升最高达1.6倍。
Cost-efficient Active Learning for Referring Image Segmentation and Grounding
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 1.6X faster description labeling of ours.