CutPaste&Find: Efficient Multimodal Hallucination Detector with Visual-aid Knowledge Base
This addresses the issue of hallucination detection for LVLM users by offering a more practical solution, though it is incremental as it builds on existing detection methods with efficiency improvements.
The paper tackles the problem of object hallucination in Large Vision-Language Models by proposing CutPaste&Find, a lightweight and training-free framework that achieves competitive detection performance on benchmarks like POPE and R-Bench while being significantly more efficient and cost-effective than existing methods.
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.