Feb. 14, 2024, 5:46 a.m. | Sungguk Cha Jusung Lee Younghyun Lee Cheoljong Yang

cs.CV updates on arXiv.org arxiv.org

In recent years, synthetic visual instructions by generative language model have demonstrated plausible text generation performance on the visual question-answering tasks. However, challenges persist in the hallucination of generative language models, i.e., the generated image-text data contains unintended contents. This paper presents a novel and scalable method for generating visually dehallucinative instructions, dubbed CAP2QA, that constrains the scope to only image contents. Our key contributions lie in introducing image-aligned instructive QA dataset CAP2QA-COCO and its scalable recipe. In our experiments, …

challenges contents cs.cv data generated generative hallucination image language language model language models novel paper performance question scalable synthetic tasks text text generation visual

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