arXivDaily arXiv每日学术速递 周一至周五更新

AI 大模型

多模态大模型

跨文本、图像、视频、音频等模态的大模型与学习方法。

2026-05-12 至 2026-05-12 共收录 203 信号源:cs.CV, cs.CL, cs.AI, cs.MM, eess.AS

1. 图文多模态 15 篇

2605.08188 2026-05-12 cs.CV cs.AI 81%

Neuroscience-Inspired Analyses of Visual Interestingness in Multimodal Transformers

受神经科学启发的多模态Transformer中视觉趣味性分析

Mathis Immertreu, Fitim Abdullahu, Thomas Kinfe, Helmut Grabner, Patrick Krauss, Achim Schilling

机构 * Cognitive Computational Neuroscience Group, Patter Recognition Lab, University Erlangen-Nürnberg(认知计算神经科学组、模式识别实验室、埃尔兰根-纽伦堡大学) IDS Institut für Data Science, ZHAW School of Engineering, Winterthur, Switzerland(IDS数据科学研究所、ZHAW工程学院、温特图尔,瑞士) Mannheim Center for Neuromodulation and Neuroprosthetics, University Hospital Mannheim, Heidelberg University(曼海姆神经调制与神经假体中心、曼海姆大学医院、海德堡大学) BGU Ludwigshafen, Germany(BGU路易斯港,德国) Physics and Cognition Group, MCNN, University Hospital Mannheim, Heidelberg University(物理与认知组、MCNN、曼海姆大学医院、海德堡大学) NeuroAI and BCI Group, MCNN, University Hospital Mannheim, Heidelberg University(神经AI与BCI组、MCNN、曼海姆大学医院、海德堡大学)

专题命中 图文多模态 :multimodal(title,abstract);分类 cs.CV、cs.AI

AI总结 研究通过多模态视觉语言模型Qwen3-VL-8B探讨视觉趣味性编码机制,发现CI信息可从最终层嵌入中线性解码,揭示了无监督下视觉趣味性的结构化编码。

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2605.10130 2026-05-12 cs.CV 79%

Thermal-Det: Language-Guided Cross-Modal Distillation for Open-Vocabulary Thermal Object Detection

Thermal-Det: 语言引导的跨模态蒸馏用于开放词汇热成像目标检测

Yasiru Ranasinghe, Elim Schenck, Florence Yellin, Shuowen Hu, Christopher Funk, Vishal M. Patel

机构 * Johns Hopkins University(约翰霍普金斯大学) Kitware DEVCOM Army Research Laboratory(国防部陆军研究实验室)

专题命中 图文多模态 :cross-modal(title,abstract);分类 cs.CV

AI总结 本文提出Thermal-Det,首个针对热成像的开放词汇检测器,通过合成数据集和跨模态蒸馏提升热成像目标检测性能,实验显示在公开基准上取得2-4%的AP提升。

Comments Accepted at CVPR 26

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2605.09242 2026-05-12 eess.IV cs.CV 79%

Cross-Modal Semantic-Enhanced Diffusion Framework for Diabetic Retinopathy Grading

跨模态语义增强的扩散框架用于糖尿病视网膜病变分级

Yiqun Wang

机构 * School of Software Engineering Beijing Jiaotong University(软件工程学院 北京交通大学)

专题命中 图文多模态 :cross-modal(title,abstract);分类 cs.CV

AI总结 本文提出CLIP引导语义扩散框架,通过整合视觉-语言预训练与扩散概率建模,解决糖尿病视网膜病变分级中的细粒度病变模式区分、分布差异和临床语义知识利用问题。

Comments 6 pages, 3 figures, 2 tables

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2411.18111 2026-05-12 cs.CV 70%

When Large Vision-Language Models Meet Person Re-Identification

当大视觉-语言模型遇见人重识别

Qizao Wang, Bin Li, Xiangyang Xue

机构 * School of Computer Science, Fudan University, Shanghai, China(复旦大学计算机科学学院,上海,中国)

专题命中 图文多模态 :cross-modal(abstract);image-text(abstract);分类 cs.CV

AI总结 本文提出LVLM-ReID框架,利用大视觉-语言模型的生成能力提升人重识别的准确性,通过语义引导交互模块生成关键外观语义特征,无需额外标注即可在多个基准上取得竞争性结果。

Comments Accepted by ICASSP 2026

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2605.08493 2026-05-12 cs.CV 70%

CapCLIP: A Vision-Language Representation Alignment Approach for Wireless Capsule Endoscopy Analysis

CapCLIP:一种用于无线胶囊内镜分析的视觉-语言表示对齐方法

Haroon Wahab, Irfan Mehmood, Hassan Ugail

机构 * School of Computer Science, AI and Electronics Faculty of Engineering and Digital Technologies(计算机科学与电子工程学院,工程与数字技术学院) School of Management Faculty of Mgmt, Law & Social Sciences(管理学院,管理、法律与社会科学学院) Centre for Visual Computing and Intelligent Systems(视觉计算与智能系统中心)

专题命中 图文多模态 :cross-modal(abstract);image-text(abstract);分类 cs.CV

AI总结 CapCLIP通过将内镜图像与临床标准术语生成的文本描述对齐,提升无线胶囊内镜分析的泛化能力和语义解释性,优于现有方法。

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2603.16253 2026-05-12 cs.CV cs.AI 66%

Grounding the Score: Explicit Visual Premise Verification for Reliable Vision-Language Process Reward Models

将评分接地:为可靠视觉语言处理奖励模型的显式视觉前提验证

Junxin Wang, Dai Guan, Weijie Qiu, Zhihang Li, Yongbo Gai, Zhengyi Yang, Mengyu Zhou, Erchao Zhao, Xiaoxi Jiang, Guanjun Jiang

机构 * Qwen Large Model Application Team, Alibaba(阿里巴巴大模型应用团队) Alibaba Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所阿里巴巴分所) Beijing University of Posts and Telecommunications(北京邮电大学)

专题命中 图文多模态 :multimodal(abstract,comments);分类 cs.CV、cs.AI

AI总结 本文提出EVPV方法,通过显式验证视觉前提提升视觉语言处理奖励模型的可靠性,实验表明其在多模态推理基准上提升了重排序准确率。

Comments 27 pages, 4 figures, 10 tables. Evaluated on VisualProcessBench and six multimodal reasoning benchmarks (LogicVista, MMMU, MathVerse-VO, MathVision, MathVista, WeMath). Includes ablations and causal analysis via controlled constraint corruption. Code: https://github.com/Qwen-Applications/EVPV-PRM

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2605.10622 2026-05-12 cs.MM cs.CV 62%

Vocabulary Hijacking in LVLMs: Unveiling Critical Attention Heads by Excluding Inert Tokens to Mitigate Hallucination

LVLMs中的词汇劫持:通过排除惰性标记来揭示关键注意力头以缓解幻觉

Yangneng Chen, Junlin Li, Weijun Yao, Xilai Ma, Guodong Du, Wenya Wang, Jing Li

机构 * Harbin Institute of Technology (Shenzhen)(哈尔滨工业大学(深圳)) Huawei Technologies Co., Ltd.(华为技术有限公司) The Hong Kong Polytechnic University(香港理工大学) Nanyang Technological University(南洋理工大学)

专题命中 图文多模态 :multimodal(abstract);分类 cs.CV、cs.MM

AI总结 本文通过分析LVLMs的注意力机制,揭示了词汇劫持现象,提出HABI方法定位惰性标记,并引入NHAR指标评估其影响,最终提出HAVAE方法增强关键注意力头以减少幻觉。

Comments Accepted by ACL 2026 Main

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2605.09802 2026-05-12 cs.CV cs.AI cs.LG 62%

CrossVL: Complexity-Aware Feature Routing and Paired Curriculum for Cross-View Vision-Language Detection

CrossVL: 用于跨视角视觉语言检测的复杂度感知特征路由与配对课程学习

Zhipeng Liu, Chunbo Luo

机构 * Department of Computer Science, University of Exeter(埃克塞特大学计算机科学系)

专题命中 图文多模态 :multimodal(abstract);分类 cs.CV、cs.AI

AI总结 本文提出CrossVL框架,结合复杂度感知路径聚合和配对课程学习,提升跨视角视觉语言模型的检测性能,实验显示其在MAVREC数据集上提升了aerial mAP并缩小了地面与空中视角的性能差距。

Comments Accepted to CVPR 2026. Code available at https://github.com/1nyourlife/Crossvl_cvpr2026

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2605.05045 2026-05-12 cs.CV cs.CL 62%

When Relations Break: Analyzing Relation Hallucination in Vision-Language Model Under Rotation and Noise

关系破裂:在旋转和噪声下的视觉语言模型关系幻觉分析

Philip Wootaek Shin, Ajay Narayanan Sridhar, Sivani Devarapalli, Rui Zhang, Jack Sampson, Vijaykrishnan Narayanan

机构 * The Pennsylvania State University(宾夕法尼亚州立大学)

专题命中 图文多模态 :multimodal(abstract);分类 cs.CV、cs.CL

AI总结 研究探讨了旋转和噪声对视觉语言模型关系推理的影响,发现轻微扰动显著降低模型性能,提示需更鲁棒的几何感知模型。

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2605.10855 2026-05-12 cs.CL 57%

Learning More from Less: Exploiting Counterfactuals for Data-Efficient Chart Understanding

从少量数据中学习更多:利用反事实以提高图表理解的数据效率

Jianzhu Bao, Haozhen Zhang, Kuicai Dong, Bozhi Wu, Sarthak Ketanbhai Modi, Zi Pong Lim, Yon Shin Teo, Wenya Wang

机构 * Nanyang Technological University(南洋理工大学) Aumovio Singapore Pte. Ltd.(Aumovio新加坡私人有限公司)

专题命中 图文多模态 :multimodal(abstract);分类 cs.CL

AI总结 本文提出ChartCF框架,通过反事实数据合成和多模态偏好优化,提升图表理解的反事实敏感性,实验表明其在少数据下表现优异。

Comments Accepted to ACL 2026 Main Conference

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2605.07574 2026-05-12 cs.CV 57%

PolarVLM: Bridging the Semantic-Physical Gap in Vision-Language Models

PolarVLM:弥合视觉语言模型中的语义-物理差距

Yuliang Li, Chu Zhou, Heng Guo, Boxin Shi, Imari Sato, Zhanyu Ma

机构 * Beijing University of Posts and Telecommunications, China(北京邮电大学) National Institute of Informatics, Japan(日本国立信息机构) Peking University, China(北京大学) The University of Tokyo, Japan(东京大学)

专题命中 图文多模态 :multimodal(abstract);分类 cs.CV

AI总结 PolarVLM通过引入极化物理参数,解决视觉语言模型在反射和透明物体等光学模糊问题中的局限,提出双流架构和分阶段训练策略,构建首个极化感知的VQA基准,实现25.4%的总体提升。

Comments 23 pages, 12 figures, including appendices

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2605.09954 2026-05-12 cs.RO cs.CV 57%

JODA: Composable Joint Dynamics for Articulated Objects

JODA:可组合的联合动力学用于连杆物体

Tianhong Gao, Cheng Yu, Yinghao Xu, Mengyu Chu

机构 * Peking University(北京大学) Ant Group, Robbyant(蚂蚁集团,Robbyant)

专题命中 图文多模态 :multimodal(abstract);分类 cs.CV

AI总结 JODA通过结构化三通道场生成关节级动力学,结合视觉和语言模型推断并优化关节动力学,实现可控的连杆物体动力学建模。

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2511.12878 2026-05-12 cs.CV cs.RO 57%

Uni-Hand: Universal Hand Motion Forecasting in Egocentric Views

Uni-Hand:面向亲身体验视角的通用手部运动预测

Junyi Ma, Wentao Bao, Jingyi Xu, Guanzhong Sun, Yu Zheng, Erhang Zhang, Xieyuanli Chen, Hesheng Wang

机构 * IRMV Lab, Shanghai Jiao Tong University(上海交通大学IMV实验室) Meta Reality Labs(Meta现实实验室) Department of Electronic Engineering, Shanghai Jiao Tong University(上海交通大学电子工程系) China University of Mining and Technology(中国矿业大学) College of Intelligence Science and Technology, National University of Defense Technology(国防科技大学智能科学与技术学院)

专题命中 图文多模态 :multi-modal(abstract);分类 cs.CV

AI总结 本文提出Uni-Hand框架,通过多模态输入、多维多目标预测和多任务赋能,实现手部在2D和3D空间的精准预测,并引入目标指标预测手腕和指尖关节点,同时预测手-物体交互状态,提升下游任务性能。

Comments Accepted by T-PAMI 2026. Code and data: https://github.com/IRMVLab/UniHand

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2605.00884 2026-05-12 cs.CV 57%

LiteVLA-H: Dual-Rate Vision-Language-Action Inference for Onboard Aerial Guidance and Semantic Perception

LiteVLA-H: 双速率视觉-语言-动作推断用于机载空中引导与语义感知

Justin williams, Kishor Datta Gupta, Roy George, Mrinmoy Sarkar

机构 * Department of Cyber Physical Systems, Clark Atlanta University(克劳克阿特拉大学网络物理系统系)

专题命中 图文多模态 :multimodal(abstract);分类 cs.CV

AI总结 LiteVLA-H通过双速率操作在边缘设备上实现高效视觉-语言-动作推断,兼顾快速动作输出与语义理解,提升空中引导与场景感知性能。

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2605.08254 2026-05-12 cs.LG cs.AI 57%

HyperTransport: Amortized Conditioning of T2I Generative Models

HyperTransport:T2I生成模型的 amortized 条件控制

Valentino Maiorca, Eleonora Gualdoni, Xavier Suau, Marco Cuturi, Luca Zappella, Pau Rodríguez

机构 * Apple(苹果公司)

专题命中 图文多模态 :cross-modal(abstract);分类 cs.AI

AI总结 HyperTransport 通过超网络框架实现对T2I生成模型的amortized条件控制,结合连续可解释强度控制和跨模态条件,提升模型可控性与效率,验证结果在多个测试概念上优于传统方法。

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2. 音频语音多模态 13 篇

2605.09906 2026-05-12 cs.AI cs.SD 88%

Separate First, Fuse Later: Mitigating Cross-Modal Interference in Audio-Visual LLMs Reasoning with Modality-Specific Chain-of-Thought

先分后融:通过模态特定的推理链减轻音频视觉大语言模型中的跨模态干扰

Xuanchen Li, Yuheng Lu, Chenrui Cui, Tianrui Wang, Zikang Huang, Yu Jiang, Long Zhou, Longbiao Wang, Jianwu Dang

机构 * Tianjin Key Laboratory of Cognitive Computing(天津认知计算实验室) Tianjin University(天津大学) Huiyan Technology Company, Ltd.(慧颜科技有限公司) Chinese Academy of Sciences(中国科学院) Tencent(腾讯)

专题命中 音频语音多模态 :cross-modal(title,abstract);audio-visual(title,abstract);分类 cs.AI

AI总结 本文提出SFFL框架,通过模态特定的推理链减少跨模态干扰,提升音频视觉问答的准确性和鲁棒性,实验显示在通用AVQA基准和跨模态幻觉基准上分别提升5.16%和11.17%。

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2605.08723 2026-05-12 cs.CV cs.MM 86%

EAR: Enhancing Uni-Modal Representations for Weakly Supervised Audio-Visual Video Parsing

EAR:增强单模表示以实现弱监督音频视觉视频解析

Huilai Li, Xiaomeng Di, Ying Xing, Yonghao Dang, Yiming Wang, Jianqin Yin

机构 * School of Intelligent Engineering and Automation, Beijing University of Posts and Telecommunications(智能工程与自动化学院,北京邮电大学) State Grid Corporation of China(国家电网公司) School of Artificial Intelligence, Beijing University of Posts and Telecommunications(人工智能学院,北京邮电大学)

专题命中 音频语音多模态 :audio-visual(title,abstract);multi-modal(abstract);cross-modal(abstract);分类 cs.CV、cs.MM

AI总结 本文提出EAR框架,通过增强伪标签生成器和AVVP模型的单模表示,提升视频解析的时序定位性能。

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2605.08762 2026-05-12 cs.SD cs.LG 85%

Omni-DeepSearch: A Benchmark for Audio-Driven Omni-Modal Deep Search

Omni-DeepSearch:一种基于音频的多模态深度搜索基准

Tao Yu, yiming ding, Shenghua Chai, Minghui Zhang, Zhongtian Luo, Xinming Wang, Xinlong Chen, Zhaolu Kang, Junhao Gong, Yuxuan Zhou, Haopeng Jin, Zhiqing Cui, Jiabing Yang, YiFan Zhang, Hongzhu Yi, Zheqi He, Xi Yang, Yan Huang, Liang Wang

机构 * CASIA UCAS BAAI Peking University(北京大学) Tsinghua University(清华大学)

专题命中 音频语音多模态 :omni-modal(title,abstract);multimodal(abstract);cross-modal(abstract)

AI总结 本文提出Omni-DeepSearch基准,用于评估基于音频的多模态深度搜索能力,通过多跳推理生成客观答案,结果显示该任务极具挑战性。

Comments 43 pages

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2601.22143 2026-05-12 cs.GR cs.CV 83%

JUST-DUB-IT: Video Dubbing via Joint Audio-Visual Diffusion

JUST-DUB-IT: 通过联合音频-视觉扩散进行视频配音

Anthony Chen, Naomi Ken Korem, Gal Zeevi, Tavi Halperin, Matan Ben Yosef, Urska Jelercic, Ofir Bibi, Or Patashnik, Daniel Cohen-Or

机构 * Tel Aviv University(特拉维夫大学)

专题命中 音频语音多模态 :audio-visual(title,abstract);multi-modal(abstract);分类 cs.CV

AI总结 本文提出JUST-DUB-IT方法,利用轻量LoRA调整基础音频-视频扩散模型,实现视频到视频的高质量配音,提升视觉保真度和唇同步性能。

Comments Project webpage available at https://justdubit.github.io

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2605.09468 2026-05-12 cs.MM 83%

Mitigating Multimodal Inconsistency via Cognitive Dual-Pathway Reasoning for Intent Recognition

通过认知双通路推理缓解多模态不一致:意图识别

Yifan Wang, Peiwu Wang, Yunxian Chi, Zhinan Gou, Kai Gao

专题命中 音频语音多模态 :multimodal(title,abstract);cross-modal(abstract);分类 cs.MM

AI总结 本文提出认知双通路推理框架,通过构建稳定语义基础和缓解高层语义冲突,提升多模态意图识别的准确性和鲁棒性。

Comments Accepted by ICMR 2026 (Main Track, Long Paper)

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2605.08805 2026-05-12 cs.CV 83%

LightAVSeg: Lightweight Audio-Visual Segmentation

LightAVSeg: 轻量级音频视觉分割

Qing Zhong, Guodong Ding, Lingqiao Liu, Zaiwen Feng, Lin Yuanbo Wu, Angela Yao

机构 * College of Informatics, Huazhong Agricultural University, Wuhan, China(华中农业大学信息学院) School of Computing, National University of Singapore, Singapore(新加坡国立大学计算机学院) School of Computer Science, Adelaide University, Australia(阿德莱德大学计算机科学学院) School of Engineering, University of Warwick, Coventry, UK(沃里克大学工程学院) Zhejiang Yuexiu University, Shaoxing, China(浙江越秀大学)

专题命中 音频语音多模态 :audio-visual(title,abstract);cross-modal(abstract);分类 cs.CV

AI总结 本文提出LightAVSeg,通过解耦设计替代重注意力机制,实现线性交互成本,提升效率,实验显示其在MS3基准上达到50.4 mIoU,参数量仅为AVSegFormer的1/7。

Comments 15 pages, 8 figures, 6 tables, Accepted to ICML 2026

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2604.24954 2026-05-12 cs.LG cs.AI cs.CV 81%

Nemotron 3 Nano Omni: Efficient and Open Multimodal Intelligence

Nemotron 3 Nano Omni:高效且开放的多模态智能

NVIDIA, :, Amala Sanjay Deshmukh, Kateryna Chumachenko, Tuomas Rintamaki, Matthieu Le, Tyler Poon, Danial Mohseni Taheri, Ilia Karmanov, Guilin Liu, Jarno Seppanen, Arushi Goel, Mike Ranzinger, Greg Heinrich, Guo Chen, Lukas Voegtle, Philipp Fischer, Timo Roman, Karan Sapra, Collin McCarthy, Shaokun Zhang, Fuxiao Liu, Hanrong Ye, Yi Dong, Mingjie Liu, Yifan Peng, Piotr Zelasko, Zhehuai Chen, Nithin Rao Koluguri, Nune Tadevosyan, Lilit Grigoryan, Ehsan Hosseini Asl, Pritam Biswas, Leili Tavabi, Yuanhang Su, Zhiding Yu, Peter Jin, Alexandre Milesi, Netanel Haber, Yao Xu, Sarah Amiraslani, Nabin Mulepati, Eric Tramel, Jaehun Jung, Ximing Lu, Brandon Cui, Jin Xu, Zhiqi Li, Shihao Wang, Yuanguo Kuang, Shaokun Zhang, Huck Yang, Boyi Li, Hongxu Yin, Song Han, Bilal Kartal, Pavlo Molchanov, Adi Renduchintala, Charles Wang, David Mosallanezhad, Soumye Singhal, Luis Vega, Katherine Cheung, Sreyan Ghosh, Yian Zhang, Alexander Bukharin, Venkat Srinivasan, Johnny Greco, Andre Manoel, Maarten Van Segbroeck, Suseella Panguliri, Rohit Watve, Divyanshu Kakwani, Shubham Pachori, Jeffrey Glick, Radha Sri-Tharan, Aileen Zaman, Khanh Nguyen, Shi Chen, Jiaheng Fang, Qing Miao, Wenfei Zhou, Yu Wang, Zaid Pervaiz Bhat, Varun Praveen, Arihant Jain, Ramanathan Arunachalam, Tomasz Kornuta, Ashton Sharabiani, Amy Shen, Wei Huang, Yi-Fu Wu, Ali Roshan Ghias, Huiying Li, Brian Yu, Nima Tajbakhsh, Chen Cui, Wenwen Gao, Li Ding, Terry Kong, Manoj Kilaru, Anahita Bhiwandiwalla, Marek Wawrzos, Daniel Korzekwa, Pablo Ribalta, Grzegorz Chlebus, Besmira Nushi, Ewa Dobrowolska, Maciej Jakub Mikulski, Kunal Dhawan, Steve Huang, Jagadeesh Balam, Yongqiang Wang, Nikolay Karpov, Valentin Mendelev, George Zelenfroynd, Meline Mkrtchyan, Qing Miao, Omri Almog, Bhavesh Pawar, Rameshwar Shivbhakta, Sudeep Sabnis, Ashrton Sharabiani, Negar Habibi, Geethapriya Venkataramani, Pamela Peng, Prerit Rodney, Serge Panev, Richard Mazzarese, Nicky Liu, Michael Fukuyama, Andrii Skliar, Roger Waleffe, Duncan Riach, Yunheng Zou, Jian Hu, Hao Zhang, Binfeng Xu, Yuhao Yang, Zuhair Ahmed, Alexandre Milesi, Carlo del Mundo, Chad Voegele, Zhiyu Cheng, Nave Assaf, Andrii Skliar, Daniel Afrimi, Natan Bagrov, Ran Zilberstein, Ofri Masad, Eugene Khvedchenia, Natan Bagrov, Borys Tymchenko, Tomer Asida, Daniel Afrimi, Parth Mannan, Victor Cui, Michael Evans, Katherine Luna, Jie Lou, Pinky Xu, Guyue Huang, Negar Habibi, Michael Boone, Pradeep Thalasta, Adeola Adesoba, Dina Yared, Christopher Parisien, Leon Derczynski, Shaona Ghosh, Wes Feely, Micah Schaffer, Radha Sri-Tharan, Jeffrey Glick, Barnaby Simkin, George Zelenfroynd, Tomasz Grzegorzek, Rishabh Garg, Aastha Jhunjhunwala, Sergei Kolchenko, Farzan Memarian, Haran Kumar, Shiv Kumar, Isabel Hulseman, Anjali Shah, Kari Briski, Padmavathy Subramanian, Joey Conway, Udi Karpas, Jane Polak Scowcroft, Annie Surla, Shilpa Ammireddy, Ellie Evans, Jesse Oliver, Tom Balough, Chia-Chih Chen, Sandip Bhaskar, Alejandra Rico, Bardiya Sadeghi, Seph Mard, Katherine Cheung, Meredith Price, Laya Sleiman, Saori Kaji, Wesley Helmholz, Wendy Quan, Michael Lightstone, Jonathan Cohen, Jian Zhang, Oleksii Kuchaiev, Boris Ginsburg, Jan Kautz, Eileen Long, Mohammad Shoeybi, Mostofa Patwary, Oluwatobi Olabiyi, Andrew Tao, Bryan Catanzaro, Udi Karpas

机构 * NVIDIA

专题命中 音频语音多模态 :multimodal(title,abstract);分类 cs.CV、cs.AI

AI总结 Nemotron 3 Nano Omni是首个原生支持音频输入的多模态模型,通过架构、数据和训练方法的改进,在所有模态上均实现了更准确的性能,同时提供更低的推理延迟和更高的吞吐量。

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2605.08175 2026-05-12 cs.CV cs.AI 73%

KARMA-MV: A Benchmark for Causal Question Answering on Music Videos

KARMA-MV:音乐视频上的因果问答基准

Archishman Ghosh, Abhinaba Roy, Dorien Herremans

机构 * AMAAI Lab, Singapore University of Technology and Design(新加坡科技设计大学AMAAI实验室)

专题命中 音频语音多模态 :cross-modal(abstract);audio-visual(abstract);分类 cs.CV、cs.AI

AI总结 KARMA-MV是一个基于2682个YouTube音乐视频构建的大规模多选问答数据集,旨在测试模型整合时序音频视觉线索和视觉到音乐影响的能力,通过因果知识图谱方法提升音乐视频因果推理性能。

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2605.09272 2026-05-12 cs.AI cs.CL cs.CV 67%

Towards Conversational Medical AI with Eyes, Ears and a Voice

面向有眼睛、耳朵和声音的对话式医疗AI

Meet Shah, Jason Gusdorf, Anil Palepu, Chunjong Park, Jack W. O'Sullivan, Vishnu Ravi, Tim Strother, Pavel Dubov, Aliya Rysbek, Toshiyuki Fukuzawa, Yana Lunts, Jan Freyberg, Michael B. Chang, Aniruddh Raghu, David Stutz, Devora Berlowitz, Eliseo Papa, Taylan Cemgil, JD Velasquez, Jack Chen, Arthur Chen, Doug Fritz, Charlie Taylor, Katya Tregubova, Jing Rong Lim, Richard Green, Sara Mahdavi, Mahvish Nagda, Jihyeon Lee, Craig Schiff, Liviu Panait, Sukhdeep Singh, Valentin Liévin, David G. T. Barrett, Hannah Gladman, Anna Cupani, Francesca Pietra, Uchechi Okereke, Katherine Tong, Clemens Meyer, Erwan Rolland, Mili Sanwalka, Michael D. Howell, Shixiang Shane Gu, Bibo Xu, Euan A. Ashley, S. M. Ali Eslami, Gregory Wayne, Pushmeet Kohli, Vivek Natarajan, Adam Rodman, Alan Karthikesalingam, Ryutaro Tanno

机构 * Google DeepMind(谷歌DeepMind) Google Research(谷歌研究) Beth Israel Deaconess Medical Center, Harvard Medical School(贝塞斯达医院, 哈佛医学院) Stanford University(斯坦福大学)

专题命中 音频语音多模态 :audio-visual(abstract);分类 cs.CV、cs.CL、cs.AI

AI总结 本文提出AI co-clinician系统,利用音频视频数据实现实时临床决策,通过TelePACES评估标准显示其在管理计划和诊断差异方面接近医生,但在体格检查和疾病特异性推理上仍有不足。

Comments Video examples are available on Youtube: https://youtu.be/y5Vaa_SN1t0, https://youtu.be/dC4icb75vLQ, and https://youtu.be/E7iEvWo-E6c

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2605.09846 2026-05-12 cs.SD cs.AI 57%

ChladniSonify: A Visual-Acoustic Mapping Method for Chladni Patterns in New Media Art Creation

ChladniSonify:一种用于新媒介艺术创作中Chladni图案的视觉-听觉映射方法

Yakun Liu, Hai Luan, Dong Liu, Zhiyu Jin

机构 * Department of Composition(作曲系) Education Information Center(教育信息中心) Department of Musicology(音乐学系)

专题命中 音频语音多模态 :audio-visual(abstract);分类 cs.AI

AI总结 本文提出ChladniSonify,通过基于Kirchhoff-Love板理论的数值编程构建数据集并利用ANSYS仿真校准,采用轻量级CNN与CBAM实现高精度低延迟的Chladni图案分类,最终构建端到端系统将识别图案映射到对应正弦波频率,实现实时交互。

Comments 9 pages, 5 figures, IEEE conference format

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2605.09656 2026-05-12 cs.RO 50%

ORICF -- Open Robotics Inference and Control Framework

ORICF -- 开源机器人推断与控制框架

Andrés Meseguer Valenzuela, Luís Miguel Bartolín Arnau

机构 * Instituto Tecnológico de Informática (ITI)(技术信息学院)

专题命中 音频语音多模态 :multimodal(abstract)

AI总结 本文提出ORICF框架,通过模块化设计实现多模态机器人推断流水线,支持边缘计算,降低计算开销和能耗,保持模块化和可重复性。

Comments Accepted in ICRA26 Workshop: 8th International Workshop on Robotics Software Engineering (RoSE 26)

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2605.09434 2026-05-12 cs.DC cs.HC cs.LG 50%

PoHAR: Understanding Hyperlocal Human Activities with Pollution Sensor Networks

PoHAR:利用污染传感器网络理解超本地人类活动

Prasenjit Karmakar, Karthik Reddy, Sandip Chakraborty

机构 * Department of Computer Science and Engineering(计算机科学与工程系) Indian Institute of Technology Kharagpur(印度理工学院克拉格普尔)

专题命中 音频语音多模态 :audio-visual(abstract)

AI总结 本文提出PoHAR框架,通过冲突自由的数据复制原语、分层聚类和基于领导者的分组推断,实现室内超本地活动检测,实验显示在设备上检测室内活动和烹饪活动的准确率分别达到97.41%和99.68%。

Comments 8 pages, 8 figures, accepted to IEEE DCOSS-IoT 2026

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2502.20213 2026-05-12 cs.LG cs.CY 50%

Mixture of Experts for Recognizing Depression from Interview and Reading Tasks

专家混合模型用于从访谈和阅读任务中识别抑郁症

Loukas Ilias, Dimitris Askounis

机构 * DSS Laboratory, School of ECE, National Technical University of Athens, Greece(国家技术大学雅典分校电子工程系DSS实验室)

专题命中 音频语音多模态 :multimodal(abstract)

AI总结 本文提出利用多模态融合和专家混合模型,从访谈和阅读任务的语音中识别抑郁症,实现87%的准确率和86.66%的F1分数。

Comments Accepted at ICASSP 2026

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3. 视频多模态 18 篇

2605.10739 2026-05-12 eess.IV cs.AI cs.CV 84%

Geospatial-Temporal Sensemaking of Remote Sensing Activity Detections with Multimodal Large Language Model

基于多模态大语言模型的遥感活动检测的时空感知

David F. Ramirez, Tim Overman, Kristen Jaskie, Andreas Spanias

机构 * SenSIP Center, School of ECEE, Arizona State University(SenSIP中心,电子与计算机工程学院,亚利桑那州立大学) Prime Solutions Group Inc(Prime Solutions Group公司) Intelligence Advanced Research Projects Activity(智能高级研究计划局)

专题命中 视频多模态 :multimodal(title);MLLM(abstract,abstract_cn);分类 cs.CV、cs.AI

AI总结 本文提出SMART-HC-VQA数据集,用于人类活动的时空分析,通过多模态大语言模型训练框架实现遥感活动的检测与推理。

Comments Accepted to 2026 SPIE Defense + Security, Automatic Target Recognition XXXVI

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2605.10732 2026-05-12 cs.CV cs.AI 81%

iPay: Integrated Payment Action Recognition via Multimodal Networks and Adaptive Spatial Prior Learning

iPay: 通过多模态网络和自适应空间先验学习实现集成支付动作识别

Kaicong Huang, Weiheng Oh, Thomas Guggisberg, Ruimin Ke

机构 * Rensselaer Polytechnic Institute(伦斯勒理工学院) Capital District Transportation Authority(卡特里奇交通局)

专题命中 视频多模态 :multimodal(title,abstract);分类 cs.CV、cs.AI

AI总结 本文提出iPay框架,结合多模态混合专家架构和自适应空间先验学习,提升公共交通车内支付动作识别的准确性和效率。

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