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thinkingReed

ColPali:使用视觉语言模型的高效文档检索 VisionThink:通过强化学习实现高效的视觉语言模型 ChartMoE:使用混合专家架构的图表理解方法 基于标记级相关性引导的压缩方法以实现高效的多模态文档理解 PACT:基于剪枝和聚类的Token减少方法 DocLayLLM:一种用于富含文本的文档理解的高效多模态大语言模型扩展方法 mPLUG-DocOwl 2:高分辨率文档理解的布局感知压缩方法 mPLUG-DocOwl 1.5 研究报告:模型架构与位置编码机制 mPLUG-DocOwl:面向无OCR文档理解的统一结构学习 QG-VTC:基于问题引导的视觉Token压缩方法 MinerU 技术报告:高精度文档内容提取解决方案 TabPedia:统一的表格理解视觉语言模型 Pix2Struct:截图解析作为视觉语言理解预训练方法 TextMonkey:用于文档理解的无OCR大型多模态模型 Swin Transformer:分层视觉Transformer Vision Transformer:将Transformer应用于图像分类任务 DocPedia:频域方法实现高分辨率文档理解 领域特定的大语言模型用于通过有标签数据监督微调的暖通空调系统故障诊断 将知识从大模型迁移到小型下游模型
StrucTexT:多模态Transformer的结构化文本理解框架
2025-06-17 · via thinkingReed
StrucTexT: Structured Text Understanding with Multi-Modal Transformers ## 背景why ### 文档智能 VRDS(Visually Rich Documents)