CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Base
Journal:
arXiv
Published Date:
Feb 18, 2025
Abstract
Large Vision-Language Models (LVLMs) have demonstrated impressive multimodal
reasoning capabilities, but they remain susceptible to hallucination,
particularly object hallucination where non-existent objects or incorrect
attributes are fabricated in generated descriptions. Existing detection methods
achieve strong performance but rely heavily on expensive API calls and
iterative LVLM-based validation, making them impractical for large-scale or
offline use. To address these limitations, we propose CutPaste\&Find, a
lightweight and training-free framework for detecting hallucinations in
LVLM-generated outputs. Our approach leverages off-the-shelf visual and
linguistic modules to perform multi-step verification efficiently without
requiring LVLM inference. At the core of our framework is a Visual-aid
Knowledge Base that encodes rich entity-attribute relationships and associated
image representations. We introduce a scaling factor to refine similarity
scores, mitigating the issue of suboptimal alignment values even for
ground-truth image-text pairs. Comprehensive evaluations on benchmark datasets,
including POPE and R-Bench, demonstrate that CutPaste\&Find achieves
competitive hallucination detection performance while being significantly more
efficient and cost-effective than previous methods.
