Fine-grained large-vision-language hallucination detection presents the challenge of locating hallucinated content in image-grounded responses and classifying them with corresponding hallucination categories. Our method for the SHROOM-Visions 2026 Shared Task explores a parameter-efficient solution by fine-tuning a lightweight token-level classification head on the frozen weights of the Qwen3-VL-8B-Instruct vision-language model. We utilized category-specific thresholds calculated based on validation character-level IoU and designed a span merging post-processing step to facilitate span localization with fewer fragmented predictions. We also establish a possible connection between the annotator agreement and the operating thresholds. Our analysis suggests that, within our experimental setup, hallucination categories that had higher inter-annotator agreement tended to have higher optimal decision thresholds whereas categories with lower agreement tended to have lower optimal decision thresholds. Our parameter-efficient method achieved ranks 18th, 16th, 16th, and 15th place on the English, French, Italian, and Chinese tracks, respectively, showing promising performance for multilingual fine-grained hallucination detection.