Cost-efficient Active Learning for Referring Image Segmentation and Grounding
Quick summary
arXiv:2608.30621v2 Announce Type: replace-cross 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 discriminativ
Key takeaways
- arXiv:2608.30621v2 Announce Type: replace-cross 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 discriminativ
Why it matters
“Cost-efficient Active Learning for Referring Image Segmentation and Grounding” shows why continuity and fallback planning matter as AI services move into operational workflows. Provider status, fault tolerance, alternate paths and user communication should be part of production design.
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