Results 51 to 60 of about 15,745,009 (280)

SimpleVQA: Multimodal Factuality Evaluation for Multimodal Large Language Models

CoRR
The increasing application of multi-modal large language models (MLLMs) across various sectors have spotlighted the essence of their output reliability and accuracy, particularly their ability to produce content grounded in factual information (e.g. common and domain-specific knowledge).
Xianfu Cheng, Wei Zhang 0021, Shiwei Zhang, Jian Yang 0030, Xiangyuan Guan, Xianjie Wu, Xiang Li 0117, Ge Zhang 0009, Jiaheng Liu, Yuying Mai, Yutao Zeng, Zhoufutu Wen, Ke Jin, Baorui Wang, Weixiao Zhou, Yunhong Lu, Tongliang Li, Wenhao Huang 0001, Zhoujun Li 0001   +18 more
openaire   +4 more sources

Hyperbolic Learning with Multimodal Large Language Models

Hyperbolic embeddings have demonstrated their effectiveness in capturing measures of uncertainty and hierarchical relationships across various deep-learning tasks, including image segmentation and active learning. However, their application in modern vision-language models (VLMs) has been limited.
Paolo Mandica, Luca Franco, Konstantinos Kallidromitis, Suzanne Petryk, Fabio Galasso   +4 more
openaire   +4 more sources

Semantic Alignment for Multimodal Large Language Models

Proceedings of the 32nd ACM International Conference on Multimedia
Research on Multi-modal Large Language Models (MLLMs) towards the multi-image cross-modal instruction has received increasing attention and made significant progress, particularly in scenarios involving closely resembling images (e.g., change captioning).
Tao Wu, Mengze Li 0001, Jingyuan Chen, Wei Ji 0008, Wang Lin, Jinyang Gao, Kun Kuang 0001, Zhou Zhao 0001, Fei Wu 0001   +8 more
openaire   +3 more sources

Understanding Open Source Large Language Models: An Exploratory Study

132140Prompted by the increasing dominance of proprietary Large Language Models (LLMs), such as OpenAI’s GPT-4 and Google’s Gemini, concerns about data privacy, accessibility and bias have led to a growing advocacy for OSLLMs.
Mou, Yongli, Sowe, Sulayman K., Cheng, Du, Kong, Lingxiao, Neumann, Alexander Tobias, Decker, Stefan   +5 more
core   +1 more source

Artificial Intelligence in Systemic Sclerosis: Clinical Applications, Challenges, and Future Directions

Arthritis Care &Research, EarlyView.
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos, José Miguel Sequí‐Sabater, Diego Benavent   +2 more
wiley   +1 more source

On opportunities and challenges of large multimodal foundation models in education

npj Science of Learning
Recently, the option to use large language models as a middleware connecting various AI tools and other large language models led to the development of so-called large multimodal foundation models, which have the power to process spoken text, music ...
Stefan Küchemann, Karina E. Avila, Yavuz Dinc, Chiara Hortmann, Natalia Revenga, Verena Ruf, Niklas Stausberg, Steffen Steinert, Frank Fischer, Martin Fischer, Enkelejda Kasneci, Gjergji Kasneci, Thomas Kuhr, Gitta Kutyniok, Sarah Malone, Michael Sailer, Albrecht Schmidt, Matthias Stadler, Jochen Weller, Jochen Kuhn   +19 more
doaj   +1 more source

QueryMintAI: Multipurpose Multimodal Large Language Models for Personal Data

IEEE Access
QueryMintAI, a versatile multimodal Language Learning Model (LLM) designed to address the complex challenges associated with processing various types of user inputs and generating corresponding outputs across different modalities.
Ananya Ghosh, K. Deepa
doaj   +1 more source

A Survey on Benchmarks of Multimodal Large Language Models

CoRR
Multimodal Large Language Models (MLLMs) are gaining increasing popularity in both academia and industry due to their remarkable performance in various applications such as visual question answering, visual perception, understanding, and reasoning. Over the past few years, significant efforts have been made to examine MLLMs from multiple perspectives ...
Li, Jian, Lu, Weiheng, Fei, Hao, Luo, Meng, Dai, Ming, Xia, Min, Jin, Yizhang, Gan, Zhenye, Qi, Ding, Fu, Chaoyou, Tai, Ying, Yang, Wankou, Wang, Yabiao, Wang, Chengjie   +13 more
openaire   +3 more sources

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Emotion Recognition from Videos Using Multimodal Large Language Models

Future Internet
The diffusion of Multimodal Large Language Models (MLLMs) has opened new research directions in the context of video content understanding and classification. Emotion recognition from videos aims to automatically detect human emotions such as anxiety and
Lorenzo Vaiani, Luca Cagliero, Paolo Garza   +2 more
doaj   +1 more source