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。

2026-08-04 至 2026-08-04 共收录 197 信号源:cs.CV, cs.CL, cs.AI, cs.MM, eess.AS

1. 多模态评测 44 篇

2608.00012 2026-08-04 cs.CL cs.AI cs.CY cs.LG 新提交 87%

Obshazard-bench: Benchmarking Multimodal Foundation Models for Real-Time Disaster Intelligence from Raw Earth Observation Streams

Obshazard-bench:面向原始地球观测流的实时灾害情报多模态基础模型基准测试

Fengxiang Wang, Qiuyang Yu, Yueying Li, Mingshuo Chen, Chengchi Fei, Kaiyi Xu, Lixin Gu, Wangxu Wei, Junchao Gong, Lipeng Ma, Jiong Wang, Fenghua Ling, Wenlong Zhang, Xue Yang, Wenjing Yang, Ben Fei, Long Lan

机构 * National University of Defense Technology(国防科技大学) Shanghai Artificial Intelligence Laboratory(上海人工智能实验室) Shanghai Jiao Tong University(上海交通大学)

专题命中 多模态评测 :multimodal(title,abstract);multimodal foundation model(title);分类 cs.CL、cs.AI

AI总结 本研究推出Obshazard-bench基准测试,评估多模态基础模型的实时灾害情报能力,发现现有模型在将原始多通道物理观测转化为符合决策需求的灾害推理方面存在显著局限。

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2608.02561 2026-08-04 cs.CV 新提交 85%

ReMiX-MAE: Learning Missing-Channel Cross-Modal Representations from RGB-Only Clinical Facial Videos for Sympathetic-Mediated Pain Assessment

ReMiX-MAE:从仅含RGB的临床面部视频中学习缺失通道跨模态表征以用于交感介导的疼痛评估

Nan Bi, Taoyue Wang, Lijun Yin, Vandana Sharma

机构 * School of Computing, Binghamton University(宾汉姆顿大学计算学院) SUNY Upstate Medical University(纽约州立大学上州医科大学)

专题命中 多模态评测 :cross-modal(title,abstract);multimodal(abstract);分类 cs.CV

AI总结 本文提出ReMiX-MAE框架,构建SMP数据集,在仅用RGB的疼痛评估任务中,其性能优于仅用RGB的基线,且在数据有限的临床场景中迁移能力更优。

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2608.01008 2026-08-04 cs.AI 新提交 85%

Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models

面向细粒度遗忘:多模态大语言模型的属性遗忘

Junkai Lin, Junkai Chen, Siqi Hou, Yuhao He, Ruiqi Liu, Chenhan Jin, Shengze Xu, Tieyong Zeng

专题命中 多模态评测 :multimodal(title,abstract);MLLM(abstract);分类 cs.AI

AI总结 针对多模态大语言模型(MLLMs)属性遗忘的细粒度需求,提出轻量级训练无关框架CLRP,通过激活修补定位因果层并应用保留感知投影,实现目标属性遗忘同时保留同一身份信息,在多种MLLMs上验证了有效性。

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2606.11385 2026-08-04 cs.CV 版本更新 85%

DeceptionX: From Multimodal Evidence to Explainable Deception Detection

DeceptionX: 基于多模态大语言模型的可解释欺骗检测

Jiayu Zhang, Shuo Ye, Jiajian Huang, Yawen Cui, Taorui Wang, Wei Xia, Zeheng Wang, Haowen Tang, Yelin Wang, Hui Ma, Zitong Yu

机构 * Great Bay University(大湾区大学) Hong Kong Polytechnic University(香港理工大学)

专题命中 多模态评测 :multimodal(title,abstract);MLLM(abstract,abstract_cn);分类 cs.CV

AI总结 提出DeceptionX框架,将欺骗检测从黑箱分类转变为可解释的观察-思考-总结推理过程,通过构建DeceptChain数据集和三阶段训练管道,在标准基准上超越现有方法,同时提供专家级可解释推理路径。

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2608.02059 2026-08-04 cs.CV cs.MM 新提交 85%

MIEScore: Human-Aligned Evaluation for Multi-Source Image Editing

MIEScore:面向多源图像编辑的人类对齐评估方法

Zitong Xu, Huiyu Duan, Xinyun Zhang, Weifei Xiong, Tianyi Zheng, Xiongkuo Min, Qiang Hu, Zhengxue Cheng, Bo Li, Guangtao Zhai

专题命中 多模态评测 :MLLM(summary_cn,abstract);multimodal(abstract);分类 cs.CV、cs.MM

AI总结 针对多源图像编辑(MIE)缺乏人类对齐评估基准的问题,研究构建了首个MIE基准MIE-Bench,并提出基于MLLM的评估模型MIEScore,其对齐人类偏好性能最优且泛化性良好。

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2608.01794 2026-08-04 cs.CV cs.AI cs.CL 新提交 85%

Illuminating Visual Identity in Universal Multimodal Embeddings

揭示通用多模态嵌入中的视觉身份

Jiawei Cao, Junyi Feng, Jiashen Hua, Ziheng Huang, Bing Deng, Kaijie Wu, Chaochen Gu, Jieping Ye

机构 * Shanghai Jiao Tong University(上海交通大学) Alibaba Group(阿里巴巴集团)

专题命中 多模态评测 :multimodal(title,abstract);分类 cs.CV、cs.CL、cs.AI

AI总结 该研究针对通用多模态嵌入缺乏视觉身份判别能力的问题,提出视觉身份判别统一形式化,构建MVEB基准,设计身份感知采样的学习框架,提升了UMEs的身份判别能力。

Comments Accepted to CVPR 2026

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2511.20544 2026-08-04 cs.CV cs.AI cs.LG 版本更新 84%

New York Smells: A Large Multimodal Dataset for Olfaction

纽约气味:一个大规模多模态数据集用于嗅觉

Ege Ozguroglu, Junbang Liang, Ruoshi Liu, Mia Chiquier, Michael DeTienne, Wesley Wei Qian, Alexandra Horowitz, Andrew Owens, Carl Vondrick

机构 * Columbia University(哥伦比亚大学) Cornell University(康奈尔大学) Osmo Labs(Osmo实验室)

专题命中 多模态评测 :multimodal(title,abstract);cross-modal(abstract);分类 cs.CV、cs.AI

AI总结 New York Smells 数据集通过多模态数据提升机器对嗅觉的感知能力,展示了视觉数据在跨模态学习中的作用。

Comments Project website at https://smell.cs.columbia.edu

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2605.25806 2026-08-04 cs.CV 版本更新 83%

An Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?

聚焦女性安全分析:多模态数据集能否增强VAD模型?

Sangeeta ., Maddikuntla Sai Prajwal, Debi Prosad Dogra, Kamalakar Vijay Thakare, Hyungjoo Jung, Ig-Jae Kim, Heeseung Choi

机构 * Indian Institute of Technology Bhubaneswar(印度理工学院巴特那分校) Artificial Intelligence and Robotics Institute, Korea Institute of Science and Technology(人工智能与机器人研究所,韩国科学技术院) Yonsei-KIST Convergence Research Institute, Yonsei University(延世大学KIST融合研究中心)

专题命中 多模态评测 :multi-modal(title,abstract);cross-modal(abstract);分类 cs.CV

AI总结 针对现有视频异常检测数据集缺乏女性中心异常样本的问题,提出包含1001个视频及文本描述的多模态基准ExtrAnom,覆盖5种犯罪类型,并验证了多模态方法在检测女性中心异常上的有效性。

Comments 15 pages, 8 figures, 6 tables

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2509.11924 2026-08-04 cs.CV cs.LG 交叉投稿 83%

Enriched text-guided variational multimodal knowledge distillation network (VMD) for automated diagnosis of plaque vulnerability in 3D carotid artery MRI

用于3D颈动脉MRI斑块易损性自动诊断的增强文本引导变分多模态知识蒸馏网络(VMD)

Bo Cao, Fan Yu, Mengmeng Feng, SenHao Zhang, Xin Meng, Yue Zhang, Zhen Qian, Jie Lu

机构 * department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University(放射医学与核医学科,宣武医院,首都医科大学) Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics(北京磁共振成像与脑信息学重点实验室) Beijing United Imaging Research Institute of Intelligent Imaging(北京智能成像联合研究院)

专题命中 多模态评测 :multimodal(title,abstract);分类 cs.CV

AI总结 该研究针对3D颈动脉MRI斑块易损性自动诊断难题,提出VMD网络,利用跨模态先验知识提升未标注图像诊断准确率,经内部数据集实验验证其有效性。

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2512.04954 2026-08-04 cs.LG hep-ex hep-ph physics.comp-ph physics.data-an 版本更新 82%

Amortized Inference of Multi-Modal Posteriors using Likelihood-Weighted Normalizing Flows

利用似然加权归一化流进行多模态后验的平均推断

Rajneil Baruah

机构 * Department of Physics, SEAS, Bennett University, Greater Noida, Uttar Pradesh, India, 201310(物理系,Bennett大学,Greater Noida,印度)

专题命中 多模态评测 :multi-modal(title,abstract);multimodal(abstract)

AI总结 本文提出利用归一化流和似然加权重要性采样进行多模态后验推断,通过高斯混合模型初始化提升重建质量。

Comments 27 pages, 11 figures, 7 tables

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2607.27278 2026-08-04 cs.CV 版本更新 81%

OVEarth-Bench: Evaluating Category Breadth and Query Diversity for Open-Vocabulary Earth Observation

OVEarth-Bench:面向开放词汇地球观测的类别广度与查询多样性评估

Kaiyu Li, Zepeng Xin, Zixuan Jiang, Jing Fu, Lanxuan Xue, Lingyu Zhang, Xiangyong Cao

专题命中 多模态评测 :MLLM(summary_cn,abstract);分类 cs.CV

AI总结 本文提出OVEarth-Bench基准,从类别广度与查询多样性两方面扩展开放词汇地球观测评估,经实验发现MLLM类方法性能最优,为该领域未来研究提供指导。

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2608.00446 2026-08-04 cs.CV cs.AI 新提交 81%

Structured Proxy Features for Multimodal NSCLC Survival Prediction from Pretreatment CT

用于基于治疗前CT的多模态非小细胞肺癌(NSCLC)生存预测的结构化代理特征

Huu Phong Nguyen, Delower Hossain, Ehsan Saghapour, Zhandos Sembay, Jake Y. Chen

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

AI总结 该研究针对NSCLC生存分层难题,用模拟衍生的6种结构化代理特征扩充多模态数据,结合TMAE模型,在Lung1队列取得优于现有方法的预测性能,验证了代理特征的互补价值。

Comments 33

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2608.00100 2026-08-04 cs.CV cs.AI 新提交 81%

SPARC-Rad: A Multimodal Benchmark Dataset and Evaluation Pipeline for Spatial and Anatomical Reasoning in Radiology Vision-Language Models

SPARC-Rad:面向放射学视觉语言模型的空间与解剖推理多模态基准数据集及评估流程

Satvik Tripathi, Mustafa Ege Seker, Kristian Quevada, Ebubechukwu D Enwerem, Pratham Khandelwal, Emine Meltem, Bera Koca, Shahriar Faghani, Jacinta Arnold, Dania Daye, Tessa S. Cook

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

AI总结 该研究开发了SPARC-Rad多模态基准数据集及评估流程,用于评估放射学VLMs的空间与解剖推理能力,为相关模型的开发与部署前评估提供支持。

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2606.22144 2026-08-04 cs.CV cs.AI 版本更新 81%

SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis

SAGE:面向多模态学习与幻觉分析的专家标注南亚胃肠内镜数据集

Niyoj Oli, Sachin Acharya, Sandesh Pokhrel, Sanjay Bhandari, Ramesh Rana, Nikesh Mani Shrestha, Ram Bahadur Gurung, Yash Raj Shrestha, Prashnna K Gyawali, Binod Bhattarai

机构 * Nepal Applied Mathematics and Informatics Institute for Research(尼泊尔应用数学与信息技术研究所) GastroIntestinal Department, Dhulikhel Hospital(杜尔基hel医院消化内科) Univesity of Lausanne(洛桑大学) University of West Virginia(西弗吉尼亚大学) University of Utah(犹他大学) University of Aberdeen(阿伯丁大学)

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

AI总结 为解决南亚地区胃肠癌诊断数据缺乏问题,构建了包含1300张图像、标注和问答对的SAGE数据集,并评估了模型在人口偏移下的性能下降。

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2608.02315 2026-08-04 cs.CV 新提交 79%

GEOID-Flood: A Large-Scale Multi-Modal Benchmark Dataset for Flood Segmentation

GEOID-Flood:用于洪水分割的大规模多模态基准数据集

Gaetano Chiriaco, Luca Barco, Andrea Bragagnolo, Claudio Rossi, Edoardo Arnaudo

机构 * Fondazione LINKS(LINKS基金会) Politecnico di Torino(都灵理工大学)

专题命中 多模态评测 :multi-modal(title,abstract);分类 cs.CV

AI总结 该研究推出GEOID-Flood大规模多模态洪水分割基准数据集,评估发现基础模型优势适度,光学-SAR融合微调效果最佳,该数据集训练的模型迁移性更优。

Comments Accepted at ECCV 2026 - Terrabytes II Workshop, 23 pages

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2608.01462 2026-08-04 cs.AI 新提交 79%

Loud or Silent? A Reusable Framework for Per-Modality Failure Analysis in Multimodal Clinical AI

大声还是沉默?一种用于多模态临床AI中模态级失败分析的可复用框架

Quang Bui, Shlok Jaiswal, Samuel Paik-Heintz, Kevin Zhou, Kaushik Madapati, Krittaphas Chaisutyakorn, Noah Dane Hebdon, Dimitrios Proios, Sebastián Andrés Cajas Ordóñez, Kacper Dobek, Boya Zhang, Aly Dhedhi, Ahram Han, Kushul Reddy Palakala, Rahul Gorijavolu, Jacques Kpodonu, Leo Anthony Celi

机构 * Massachusetts Institute of Technology(麻省理工学院) American International School Vienna(维也纳美国国际学校) Neuqua Valley High School(纽夸谷高中) North Hollywood High School(北好莱坞高中) Hopewell Valley Central High School(霍普韦尔谷中央高中) University of California, Berkeley(加州大学伯克利分校) Siriraj Hospital(诗里拉吉医院) Harvard University(哈佛大学) Agency for Science, Technology and Research(新加坡科技研究局) Johns Hopkins University(约翰斯·霍普金斯大学) University of Geneva(日内瓦大学) Poznan University of Technology(波兹南理工大学)

专题命中 多模态评测 :multimodal(title,abstract);分类 cs.AI

AI总结 该研究提出一种模型无关的多模态临床AI模态级失败分析框架,可复用且仅用部署可观测信号,经植入真实值和MIMIC-IV队列验证,能定位失败模态、区分失败类型,为心脏基础模型部署提供参考。

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2608.00074 2026-08-04 cs.CV 新提交 79%

Explainable Multimodal AI for Adaptive Calibration of Archaeological Sensing Workflows

面向考古传感工作流自适应校准的可解释多模态人工智能

Nevio Dubbini, Daniel P. van Helden, Claudia Sciuto, Martina Naso, Arthur Leck, Clement Joubert, Heeli C. Schechter, Remy Chapoulie, Gabriele Gattiglia

专题命中 多模态评测 :multimodal(title,abstract);分类 cs.CV

AI总结 本文提出一种可解释多模态AI框架,用于考古传感工作流的自适应校准、质量评估与采集支持,经多模态考古数据集验证可实现跨模态稳健质量评估。

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2605.05938 2026-08-04 cs.AI 版本更新 79%

ICU-Bench:Benchmarking Continual Unlearning in Multimodal Large Language Models

ICU-Bench: 多模态大语言模型持续反学习基准测试

Yuhang Wang, Wenjie Mei, Junkai Zhang, Guangyu He, Zhenxing Niu, Haichang Gao

机构 * School of Computer Science and Technology(计算机科学与技术学院)

专题命中 多模态评测 :multimodal(title,abstract);分类 cs.AI

AI总结 本文提出ICU-Bench,用于评估多模态大语言模型在持续隐私删除中的性能,包含1000个敏感资料和9500张图像,引入新指标分析遗忘效果与稳定性,揭示现有方法在持续场景下的局限。

Comments 21 pages, 11 figures, and 9 tables

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2602.16144 2026-08-04 cs.CL cs.LG 版本更新 79%

Missing-by-Design: Certifiable Modality Deletion for Revocable Multimodal Sentiment Analysis

缺失-by-设计:可撤销多模态情感分析的可验证模态删除

Rong Fu, Ziming Wang, Chunlei Meng, Jiekai Wu, Kangan Qian, Hao Zhang, Simon Fong

机构 * University of Macau(澳门大学) Zhejiang University(浙江大学) Fudan University(复旦大学) Shanghai AI Laboratory(上海人工智能实验室) Juntendo University(立命馆大学) Tsinghua University(清华大学) University of Chinese Academy of Sciences(中国科学院大学)

专题命中 多模态评测 :multimodal(title,abstract);分类 cs.CL

AI总结 本文提出MBD框架,通过结构化表示学习和可验证参数修改流程,实现可撤销多模态情感分析中的模态删除,实验表明其在不完整输入下具有强预测性能,并实现隐私与效用的平衡。

Comments 21 pages, 6 figures. In the previous version, Juntendo University was erroneously listed as the affiliation; we must clarify that this paper has absolutely no relation to Juntendo University. Therefore, we have replaced this affiliation in the new version

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2608.01730 2026-08-04 cs.CV 新提交 77%

Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency

学习关注何处与如何判断:具备质量感知显著性的分辨率无关图像质量评估

Hakan Emre Gedik, Shashank Gupta, Alan Bovik

机构 * The University of Texas at Austin(德克萨斯大学奥斯汀分校) University of Colorado Boulder(科罗拉多大学博尔德分校)

专题命中 多模态评测 :MLLM(abstract,abstract_cn);multimodal(abstract);分类 cs.CV

AI总结 提出基于CLIP的多尺度补丁驱动模型ReLIQS,解决无参考图像质量评估的分辨率适配、计算效率等问题,在多类基准上泛化能力优于主流基线且成本相当或更低。

Comments Accepted to the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026

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2608.01328 2026-08-04 cs.CL cs.AI cs.CV 新提交 75%

LongChart VQA: A Comprehensive Benchmark for MLLMs with Complex Multi-Chart Reasoning

LongChart VQA:面向具备复杂多图表推理能力的多模态大语言模型的综合基准

Ziyan Xiao, Yinghao Zhu, Wenting Zhang, Heaju Kim, Lequan Yu

机构 * The University of Hong Kong(香港大学)

专题命中 多模态评测 :multimodal(abstract);MLLM(abstract);分类 cs.CV、cs.CL、cs.AI

AI总结 LongChart VQA是面向具备复杂多图表推理能力的MLLMs的综合基准,该基准含平均6.5张图像与31.2个问题,评估10个SOTA MLLMs发现其准确率随计算复杂性提升下降,为多图表推理研究指明方向。

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2608.00068 2026-08-04 cs.CV 新提交 70%

SafeBuild-Bench: A Temporal-Robust Construction Safety Benchmark with Graph-Enhanced Data Mining

SafeBuild-Bench:一种具有图增强数据挖掘能力的时序鲁棒建筑安全基准

Yi Cui, Zilin Wang, Yijie Xu, Qianyi Cai, Huizai Yao, Shuai Jiang, Bingzhuo Zhong, Hui Xiong

机构 * Hong Kong University of Science and Technology (Guangzhou)(香港科技大学(广州)) The Hong Kong University of Science and Technology(香港科技大学)

专题命中 多模态评测 :multimodal(abstract);image-text(abstract);分类 cs.CV

AI总结 研究人员构建了图增强数据挖掘的时序鲁棒建筑安全基准SafeBuild-Bench,开发可扩展验证的GEMS流水线,发现现有多模态大语言模型对建筑安全理解仍不足。

Comments Accepted by KDD 2026. 12 pages, 6 figures

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2603.25538 2026-08-04 cs.LG cs.SE 版本更新 67%

ARMOR: A Robust Self-Supervised Framework for Root Cause Analysis in Microservices under Missing Modality

面向缺失数据的多模态融合用于统一微服务故障管理

Wenzhuo Qian, Hailiang Zhao, Ziqi Wang, Zhipeng Gao, Jiayi Chen, Zhiwei Ling, Shuiguang Deng

机构 * Zhejiang University(浙江大学)

专题命中 多模态评测 :multimodal(abstract);cross-modal(abstract)

AI总结 本文提出ARMOR框架,通过自监督学习解决微服务故障管理中多模态数据缺失问题,提升异常检测、故障分类和根本原因定位的性能。

Comments Accepted by the Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering (ASE '26), 2026. This is the authors' version of the work; the definitive Version of Record is forthcoming

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2608.00872 2026-08-04 cs.AI cs.CR cs.CV 新提交 62%

Similarity Weighted Aggregation with Global Differential Privacy for Federated Brain Lesion Segmentation

面向联邦脑病灶分割的结合全局差分隐私的相似度加权聚合

Muhammad Irfan Khan, Eero Lehtonen, Joni Obradovic, Elina Kontio, Esa Alhoniemi, Suleiman A. Khan, Mojtaba Jafaritadi

机构 * Turku University of Applied Sciences(图尔库应用科学大学)

专题命中 多模态评测 :multi-modal(abstract);分类 cs.CV、cs.AI

AI总结 本研究提出DP-SimAgg框架,结合相似度加权聚合与服务器端差分隐私,在FeTS 2022数据集上验证其可在保护隐私的同时保持脑病灶分割的竞争力性能。

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2506.02976 2026-08-04 cs.CV cs.AI 版本更新 62%

Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge

利用深度学习评估视网膜退化:对MARIO挑战的全面分析

Rachid Zeghlache, Ikram Brahim, Pierre-Henri Conze, Mathieu Lamard, Mohammed El Amine Lazouni, Zineb Aziza Elaouaber, Leila Ryma Lazouni, Christopher Nielsen, Ahmad O. Ahsan, Matthias Wilms, Nils D. Forkert, Lovre Antonio Budimir, Ivana Matovinović, Donik Vršnak, Sven Lončarić, Philippe Zhang, Weili Jiang, Yihao Li, Yiding Hao, Markus Frohmann, Patrick Binder, Marcel Huber, Taha Emre, Teresa Finisterra Araújo, Marzieh Oghbaie, Hrvoje Bogunović, Amerens A. Bekkers, Nina M. van Liebergen, Hugo J. Kuijf, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Alberto J. Beltrán-Carrero, Juan J. Gómez-Valverde, Javier Torresano-Rodríquez, Álvaro Caballero-Sastre, María J. Ledesma Carbayo, Yosuke Yamagishi, Yi Ding, Robin Peretzke, Alexandra Ertl, Maximilian Fischer, Jessica Kächele, Sofiane Zehar, Karim Boukli Hacene, Thomas Monfort, Béatrice Cochener, Mostafa El Habib Daho, Anas-Alexis Benyoussef, Gwenolé Quellec

机构 * University of Western Brittany, Brest, France University of Tlemcen, Algeria Ophthalmology Department, CHRU Brest, Brest, France Imperial College London, United Kingdom Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan Evolucare Technologies, France College of Computer Science, Sichuan University, China Medical University of Vienna, Austria TNO, The Hague, The Netherlands Image Sciences Institute, UMC Utrecht, Utrecht, The Netherlands Johannes Kepler University Linz, Austria University of Zagreb, Faculty of Electrical Engineering Department of Radiology, University of Calgary, Calgary, AB, Canada Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada Department of Pediatrics, University of Calgary, Calgary, AB, Canada Department of Community Health Sciences, University of Calgary, Calgary, AB, Canada Department of Clinical Neuroscience, University of Calgary, Calgary, AB, Canada University of Calgary, Calgary, AB, Canada German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing, Germany Medical Faculty Heidelberg, Heidelberg University, Germany Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Spain Ophthalmology Service of the Provincial Ophthalmic Institute, Hospital Universitario Gregorio Marañón, Madrid, Spain University of Edinburgh, Scotland Lung Institute, Faculty of Medicine, Imperial College London, United Kingdom Hawkes Institute, Department of Computer Science, University College London, London, United Kingdom

专题命中 多模态评测 :multi-modal(abstract);分类 cs.CV、cs.AI

AI总结 本文通过MARIO挑战展示了深度学习在AMD监测中的应用,验证了AI在检测AMD进展方面的有效性,但尚未实现对未来演变的预测。

Comments MARIO-MICCAI-CHALLENGE 2024

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2608.02589 2026-08-04 cs.CV 新提交 57%

CAPEval: A Decoupled Caption Evaluation across Understanding and Generation

CAPEval:跨理解与生成的解耦式标题评估

Zhipeng Liu, Haochen Wang, Zhaoxiang Zhang

机构 * University of Chinese Academy of Sciences(中国科学院大学) Institute of Automation, Chinese Academy of Sciences(中国科学院自动化研究所)

专题命中 多模态评测 :multimodal(abstract);分类 cs.CV

AI总结 本研究提出解耦式标题评估基准CAPEval,将标题质量分为覆盖度与精确度,发现二者分别对应理解与生成任务性能,为标题生成器的选择优化提供指导。

Comments 21 pages, 8 figures. Code and dataset will be available at https://liuzhipenggg.github.io/CAPEval/

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2608.02192 2026-08-04 cs.CV 新提交 57%

T$^2$exture: Sparsely Perturbed Thermal-to-Texture Imaging

T²exture:稀疏扰动热成像转纹理成像

Jiashuo Chen, Cheng Dai, Yanan Hu, Fanglin Bao

专题命中 多模态评测 :cross-modal(abstract);分类 cs.CV

AI总结 针对现有热纹理成像方法的缺陷,提出T²exture框架,通过两阶段重建实现稀疏主动采集成像下的热纹理序列,在模拟基准上参数增量小且PSNR提升显著,纹理与结构恢复效果优于VFI基线。

Comments 13 pages, 7 figures

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2608.01977 2026-08-04 cs.CV 新提交 57%

SVGEval: A Vision-Grounded Framework for Perceptual-Quality Benchmarking and Evaluation in Text-to-SVG Generation

SVGEval:用于文本到SVG生成中感知质量基准测试与评估的视觉基础框架

Yiming Wang, Ye Chen, Hanqi Chen, Bingbing Ni

机构 * Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)(广东省人工智能与数字经济实验室(深圳)) School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University(上海交通大学集成电路学院(信息科学与工程学院)) Zhiyuan College, Shanghai Jiao Tong University(上海交通大学致远学院)

专题命中 多模态评测 :multimodal(abstract);分类 cs.CV

AI总结 研究针对文本到SVG生成评估的缺陷,提出视觉基础的SVGEval基准,发现多模态模型在几何布局判断上的差距,训练出可解释的SVG质量评分器,为SVG生成评估改进提供支撑。

Comments Accepted by ECCV 2026

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2608.01848 2026-08-04 cs.CV 新提交 57%

Decoupling semantics from vision: A framework for faithful visual-text compression evaluation

将语义与视觉解耦:一种用于可靠视觉-文本压缩评估的框架

Yonghan Gao, Zehong Chen, Lijian Xu, Jingzhi Chen, Jingwei Guan, Xingyu Zeng

机构 * Shenzhen University of Advanced Technology(深圳先进技术大学) Shenzhen Technology University(深圳技术大学)

专题命中 多模态评测 :multimodal(abstract);分类 cs.CV

AI总结 本研究针对现有视觉-文本压缩评估依赖下游任务性能的缺陷,提出解耦语义与视觉的评估框架,引入ZeroSense基准消除文本依赖,实验证实VTC质量与下游任务准确率存在显著差异。

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2608.01407 2026-08-04 cs.CV 新提交 57%

Training-Free Out-of-Distribution Detection for Pathology Whole-Slide Images

面向病理全切片图像的无分布外检测训练方法

Sabri Mustafa Kahya, Richard R. Chen, Muhammet Sami Yavuz, Jerry Jierui Lou, Akanimoh Adeleye, Haci Ali Kahya, Jana Lipkova

专题命中 多模态评测 :multimodal(abstract);分类 cs.CV

AI总结 本文提出基于视觉-语言病理基础模型的无训练多模态OOD检测器ZIO,在14700余张病理WSI上优于40种现有OOD方法,为临床AI安全部署提供支撑。

Comments Under review. The code is available in https://github.com/muskahya/ZIO

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