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arXiv 2403.08776cs.CVcs.AI
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利用基于聊天的视觉大语言模型进行多模态上下文外检测

Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection

The Graduate University for Advanced Studies, SOKENDAI(总合研究大学院大学( Sokendai));National Institute of Informatics(情报通信研究机构信息学研究所);The University of Tokyo(东京大学)

Fatma Shalabi, Hichem Felouat, Huy H. Nguyen, Isao Echizen

AI总结:

本研究探讨了视觉大语言模型在多模态上下文外检测中的能力,发现微调能显著提升其检测准确性,并在NewsCLIPpings数据集上验证了效果。

摘要:

上下文外(OOC)检测是一项具有挑战性的任务,涉及识别与呈现它们的上下文不相关的图像和文本。大型视觉语言模型(LVLMs)在各种任务中表现出色,包括图像分类和文本生成。然而,它们在多模态OOC检测任务中的熟练程度尚不清楚。在本文中,我们研究了LVLMs检测多模态OOC的能力,并表明这些模型在未经微调的情况下无法在OOC检测任务上达到高精度。然而,我们证明在多模态OOC数据集上微调LVLMs可以进一步提高其OOC检测准确性。为了评估LVLMs在OOC检测任务上的性能,我们在NewsCLIPpings数据集(一个大型多模态OOC数据集)上微调了MiniGPT-4。我们的结果表明,在NewsCLIPpings数据集上微调MiniGPT-4显著提高了该数据集中的OOC检测准确性。这表明微调可以显著提高LVLMs在OOC检测任务上的性能。

英文摘要:

Out-of-context (OOC) detection is a challenging task involving identifying images and texts that are irrelevant to the context in which they are presented. Large vision-language models (LVLMs) are effective at various tasks, including image classification and text generation. However, the extent of their proficiency in multimodal OOC detection tasks is unclear. In this paper, we investigate the ability of LVLMs to detect multimodal OOC and show that these models cannot achieve high accuracy on OOC detection tasks without fine-tuning. However, we demonstrate that fine-tuning LVLMs on multimodal OOC datasets can further improve their OOC detection accuracy. To evaluate the performance of LVLMs on OOC detection tasks, we fine-tune MiniGPT-4 on the NewsCLIPpings dataset, a large dataset of multimodal OOC. Our results show that fine-tuning MiniGPT-4 on the NewsCLIPpings dataset significantly improves the OOC detection accuracy in this dataset. This suggests that fine-tuning can significantly improve the performance of LVLMs on OOC detection tasks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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