HalluSegBench: Counterfactual Visual Reasoning for Segmentation
  Hallucination Evaluation
Authors: name: Xinzhuo Li, name: Adheesh Juvekar, name: Xingyou Liu, name: Muntasir Wahed, name: Kiet A. Nguyen, name: Ismini Lourentzou
Organization: PLAN Lab @University of Illinois Urbana-Champaign

Recent progress in vision-language segmentation has significantly advanced
grounded visual understanding. However, these models often exhibit
hallucinations by producing segmentation masks for objects not grounded in the
image content or by incorrectly labeling irrelevant regions. Existing
evaluation protocols for segmentation hallucination primarily focus on label or
textual hallucinations without manipulating the visual context, limiting their
capacity to diagnose critical failures. In response, we introduce
HalluSegBench, the first benchmark specifically designed to evaluate
hallucinations in visual grounding through the lens of counterfactual visual
reasoning. Our benchmark consists of a novel dataset of 1340 counterfactual
instance pairs spanning 281 unique object classes, and a set of newly
introduced metrics that quantify hallucination sensitivity under visually
coherent scene edits. Experiments on HalluSegBench with state-of-the-art
vision-language segmentation models reveal that vision-driven hallucinations
are significantly more prevalent than label-driven ones, with models often
persisting in false segmentation, highlighting the need for counterfactual
reasoning to diagnose grounding fidelity.