Counterfactual Segmentation Reasoning: Diagnosing and Mitigating Pixel-Grounding Hallucination

Xinzhuo Li; Adheesh Juvekar; Jiaxun Zhang; Xingyou Liu; Muntasir Wahed; Kiet A. Nguyen; Yifan Shen; Tianjiao Yu; Ismini Lourentzou

arXiv · 2506.21546

Counterfactual Segmentation Reasoning: Diagnosing and Mitigating Pixel-Grounding Hallucination

Xinzhuo Li·Adheesh Juvekar·Jiaxun Zhang·Xingyou Liu·Muntasir Wahed·Kiet A. Nguyen·Yifan Shen·Tianjiao Yu·Ismini Lourentzou

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Abstract

Segmentation Vision-Language Models (VLMs) have significantly advanced grounded visual understanding, yet they remain prone to pixel-grounding hallucinations, producing masks for incorrect objects or for objects that are entirely absent. Existing evaluations rely almost entirely on text- or label-based perturbations, which check only whether the predicted mask matches the queried label. Such evaluations overlook the spatial footprint and severity of hallucination and therefore fail to reveal vision-driven hallucinations, which are more challenging and more prevalent. To address this gap, we formalize the task of Counterfactual Segmentation Reasoning (CSR), where a model must segment the referenced object in the factual image and abstain in its counterfactual counterpart. To support this task, we curate HalluSegBench, the first large-scale benchmark to diagnose referring and reasoning expression segmentation hallucinations using controlled visual counterfactuals, alongside new evaluation metrics that measure hallucination severity and disentangle vision- and language-driven failure modes. We further introduce RobustSeg, a segmentation VLM trained with counterfactual fine-tuning (CFT) to learn when to segment and when to abstain. Experimental results confirm RobustSeg reduces hallucinations by 30%, while improving segmentation performance on FP-RefCOCO(+/g).

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Xinzhuo Li, Adheesh Juvekar, Jiaxun Zhang, Xingyou Liu, Muntasir Wahed, Kiet A. Nguyen, Yifan Shen, Tianjiao Yu, Ismini Lourentzou. 2026-04-23. Counterfactual Segmentation Reasoning: Diagnosing and Mitigating Pixel-Grounding Hallucination. https://arxiv.org/abs/2506.21546

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