arXiv:2502.15389
The Role of Background Information in Reducing Object Hallucination in Vision-Language Models: Insights from Cutoff API Prompting
원문 초록
Vision-Language Models (VLMs) occasionally generate outputs that contradict input images, constraining their reliability in real-world applications. While visual prompting is reported to suppress hallucinations by augmenting prompts with relevant area inside an image, the effectiveness in terms of the area remains uncertain. This study analyzes success and failure cases of Attention-driven visual prompting in object hallucination, revealing that preserving background context is crucial for mitigating object hallucination.
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SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding
Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models
Retrieval Visual Contrastive Decoding to Mitigate Object Hallucinations in Large Vision-Language Models
Reducing Object Hallucination in Large Audio-Language Models via Audio-Aware Decoding
Mitigating Object Hallucinations via Sentence-Level Early Intervention
토론
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