Large Vision-Language Models (LVLMs) persistently suffer from object hallucinations, generating plausible yet factually incorrect outputs that fabricate objects non-existent in the visual input. Existing mitigation methods typically fall into three paradigms: fine-tuning with targeted data, which incurs prohibitive com...
Yuan-Zhi Xu, Qian Gao, Jun Fan et al.· 0 citations
Large vision-language models can recognize the objects and attributes in a crowded scene yet assign an attribute to the wrong same-class instance. Generic visual-question-answering accuracy marks the response as wrong, while object-hallucination metrics may regard both the object and attribute as image-supported; neith...
Multimodal large language models generate natural-language responses from visual inputs, yet may mention objects absent from an image. In medication assistance, accessible perception, and environmental decision-making, such hallucinations can create real-world safety risks. We propose Semantic-Spatial Agreement Verific...
Zi-Heng Ren, Qian Gao, Jun Fan et al.· arXiv.org· 0 citations
This paper proposes Dual-Stream Cross-Anchor Correction (DSCC), which injects object-level visual anchors into the language model itself during fine-tuning and reaches the long-caption, low-hallucination region.
LingKai Bu, Qian Gao, Jun Fan et al.· 0 citations
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