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English(EN) MINOS: A Multimodal Evaluation Model for Bidirectional Generation Between Image and Text

MINOS模型在图像-文本任务上实现了SOTA跨模态评估

研究人员推出MINOS,一个新颖的跨模态评估模型,旨在评估双向图像和文本生成的质量。与依赖大型、未经整理的数据集的先前方法不同,MINOS在名为Minos-57K的精心构建的数据集上进行了训练,该数据集经过了严格的质量控制。这种方法使MINOS在16个非领域数据集上实现了图像到文本和文本到图像任务的最新性能,即使训练数据比先前模型少。 AI

影响 引入了一个新的跨模态评估基准和模型,有望改进未来的模型开发。

排序理由 这是一篇介绍用于跨模态评估的新模型和数据集的研究论文。

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

MINOS模型在图像-文本任务上实现了SOTA跨模态评估

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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Junzhe Zhang, Huixuan Zhang, Xinyu Hu, Li Lin, Mingqi Gao, Shi Qiu, Xiaojun Wan ·

    MINOS:图像与文本之间双向生成的跨模态评估模型

    arXiv:2506.02494v2 Announce Type: replace Abstract: Evaluation is important for multimodal generation tasks, while traditional multimodal evaluation metrics suffer from several limitations. With the rapid progress of MLLMs, there is growing interest in applying MLLMs to build gen…