AI RESEARCH
SetCon: Towards Open-Ended Referring Segmentation via Set-Level Concept Prediction
arXiv CS.CV
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ArXi:2605.20110v1 Announce Type: new Referring segmentation grounds natural-language queries to pixel-level masks, but extending it to complex scenarios with multiple instances, cross-category groups, or open-ended target sets remains challenging. Previous Large Vision Language Model (LVLM)-based methods represent referred targets with one or special tokens sequentially, treating multiple targets as separate outputs rather than a coherent set and offering little incentive to capture set-level properties such as completeness and mutual exclusivity.