跨越数字与拍摄场景的OCR解析新框架，攻克透视畸变和复杂排版难题，自动化文档处理更精准。
arXiv:2608.12898v1 Announce Type: new Abstract: Document parsing aims to transform unstructured documents into structured and machine-readable represe…
热成像与纹理重建结合新思路，稀疏扰动实现高质量跨模态成像，视觉领域前沿突破
arXiv:2608.02192v1 Announce Type: new Abstract: Thermal imaging remains effective under adverse illumination, yet passive long-wave infrared (LWIR) me…
无监督域对齐新方法，跨模态迁移助力医学影像分析。
arXiv:2607.21546v1 Announce Type: new Abstract: Multimodal based approaches often outperform single modality approaches in downstream tasks as the dif…
大模型跨界航空预测，融合飞行轨迹数据，精准预判航班延误，AI应用新场景。
arXiv:2510.23636v4 Announce Type: replace-cross Abstract: Flight delay prediction has become a key focus in air traffic management (ATM), as delays re…
语言指令精准引导医学图像分割，多级对比对齐让病灶识别更智能。
arXiv:2412.13533v4 Announce Type: replace Abstract: Medical image segmentation is a fundamental task in numerous medical engineering applications. Rec…
多模态持续学习藏有“隐性遗忘”：模型精度不变，但跨模态对齐已悄悄失效，值得关注。
arXiv:2607.02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard co…
未配对数据也能挖掘生物标志物？CALM提出可解释跨模态对齐新方法，入选MICCAI 2026。
arXiv:2607.01656v1 Announce Type: new Abstract: The interaction between brain structure and genetic influences is key to understanding neuropsychiatri…
一张图胜过千言万语，但更聪明的做法是只保留最关键的部分——看SEPS如何给视觉模型“瘦身”还能对齐得更准。
arXiv:2511.01390v2 Announce Type: replace-cross Abstract: Fine-grained cross-modal alignment aims to establish precise local correspondences between v…
提出注意力动态对齐与偏好微调方法，从内部注意力演化破解多模态幻觉难题
arXiv:2606.31054v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating conten…
参数高效的跨模态MRI分割新方法，兼顾精度与效率，医学影像AI前沿值得一看
arXiv:2603.17576v3 Announce Type: replace Abstract: Precise localization and delineation of brain tumors using magnetic resonance imaging (MRI) are es…
大模型如何给视觉模型当老师？ICML 2026探讨细粒度知识跨模态迁移，让AI理解更精准。
arXiv:2606.27527v1 Announce Type: cross Abstract: Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pr…
多模态3D MRI融合局部与全局上下文，揭秘跨模态建模新思路，医学影像AI进阶必读。
arXiv:2606.26894v1 Announce Type: new Abstract: Brain MRI poses a fundamental challenge for machine learning: models must learn from high-dimensional …
双向视觉推理评测基准，系统性剖析多模态大模型与图像生成模型的盲区，推动跨模态理解对齐。
arXiv:2601.17037v2 Announce Type: replace-cross Abstract: We investigate visual reasoning limitations of both multimodal large language models (MLLMs)…
利用心电图生成心脏磁共振影像做预训练，巧妙融合跨模态生成与疾病分类，KDD 2026 值得一读
arXiv:2601.20904v3 Announce Type: replace-cross Abstract: Cardiac Magnetic Resonance (CMR) imaging provides a comprehensive assessment of cardiac stru…
最新论文提出通过logit空间对比对齐实现跨模态生物语言模型，提升序列设计与变异评分效果。
arXiv:2606.18703v1 Announce Type: new Abstract: Pretrained biological language models expose per-token probability distributions through masked-token …
多模态大模型统一检测并解释AI生成内容，跨模态鉴别新范式。
arXiv:2507.14632v4 Announce Type: replace Abstract: The rapid advancement of generative AI has substantially improved image and video synthesis, ampli…
统一多模态潜在扩散模型实现任意模态间的连贯生成，无需配对数据，跨模态生成能力惊艳
arXiv:2606.16408v1 Announce Type: new Abstract: We introduce MUNI, an end-to-end multimodal latent diffusion framework for any-to-any generation that …
告别一刀切融合，提出针对时间事件建模的跨模态表示对齐新范式，精准提升多模态生存预测性能。
arXiv:2606.15038v1 Announce Type: new Abstract: Accurate time-to-event (TTE) prediction from multimodal clinical data remains challenging due to modal…
端到端语音LLM性能退化难题，这篇论文提出跨模态蒸馏方法对齐能力，直击行业痛点。
arXiv:2603.24596v3 Announce Type: replace-cross Abstract: While the shift from cascaded dialogue systems to end-to-end (E2E) speech Large Language Mod…
多模态先验注入表示空间去噪，RepFusion实现更鲁棒的跨模态表征融合。
arXiv:2606.14700v1 Announce Type: new Abstract: Large language models (LLMs) are widely used in text-to-image (T2I) systems, but they are typically li…