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English(EN) ClearText-Video: A Large-Scale Text-Centric Video Dataset Bridging Video Restoration and Scene-Text Enhancement

新的ClearText-Video数据集探究MLLM在低质量视频中的文本阅读能力

研究人员推出了ClearText-Video (CTVid),一个新大规模数据集,旨在评估多模态大语言模型 (MLLM) 在不同质量条件下处理以文本为中心的视频理解能力。CTVid包含超过4600个视频,拥有超过160万个人工验证的场景文本标注以及中英文问答对共22万个。该数据集包含视频的降质和修复版本,用于测试以文本为中心的视频修复和多质量视频问答,结果表明视觉增强并不总是能提高MLLM的文本保真度或推理性能。 AI

影响 该数据集将使研究人员能够更好地理解和提高MLLM在真实世界低质量视频数据上的性能。

排序理由 该条目描述了一个新的数据集和相关的研究论文,发布在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的ClearText-Video数据集探究MLLM在低质量视频中的文本阅读能力

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该条目描述了一个新的数据集和相关的研究论文,发布在arXiv上。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Jinlong Li, Jiaming Ding, Dingfu Lu, Malcolm Hsiu, Chuang Ke, Kangning Yang, Bochen Guan, Lan Fu, Jie Cai, Huiming Sun, Zibo Meng ·

    ClearText-Video:一个大规模以文本为中心的视频数据集,连接视频修复与场景文本增强

    arXiv:2608.28784v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have recently made strong progress in visual--linguistic understanding. However, their performance on text-centric video reasoning remains highly sensitive to input quality. Real-world user-p…