Counterfactual Segmentation Reasoning: Diagnosing and Mitigating Pixel-Grounding Hallucination

TL;DR

This paper introduces a new benchmark and method for diagnosing and reducing hallucinations in segmentation vision-language models through counterfactual reasoning and fine-tuning.

Contribution

It formalizes the task of Counterfactual Segmentation Reasoning, creates the HalluSegBench benchmark, and proposes RobustSeg with counterfactual fine-tuning to mitigate hallucinations.

Findings

RobustSeg reduces hallucinations by 30%.

HalluSegBench enables diagnosis of vision-driven hallucinations.

Counterfactual fine-tuning improves segmentation performance.

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).