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As Artificial Intelligence (AI) has developed rapidly over the past few decades, the new generation of AI, Large Language Models (LLMs) trained on massive datasets, has achieved ground-breaking performance in many applications. Further…

Computer Vision and Pattern Recognition · Computer Science 2024-10-29 Yijiashun Qi , Shuzhang Cai , Zunduo Zhao , Jiaming Li , Yanbin Lin , Zhiqiang Wang

Large language models (LLMs), such as ChatGPT/GPT-4, have proven to be powerful tools in promoting the user experience as an AI assistant. The continuous works are proposing multi-modal large language models (MLLM), empowering LLMs with the…

Computation and Language · Computer Science 2023-10-23 Ziqiang Zheng , Jipeng Zhang , Tuan-Anh Vu , Shizhe Diao , Yue Him Wong Tim , Sai-Kit Yeung

Vision-threatening eye diseases pose a major global health burden, with timely diagnosis limited by workforce shortages and restricted access to specialized care. While multimodal large language models (MLLMs) show promise for medical image…

Maritime Multi-Scene Recognition is crucial for enhancing the capabilities of intelligent marine robotics, particularly in applications such as marine conservation, environmental monitoring, and disaster response. However, this task…

Computer Vision and Pattern Recognition · Computer Science 2025-03-11 Xinyu Xi , Hua Yang , Shentai Zhang , Yijie Liu , Sijin Sun , Xiuju Fu

Fine-grained recognition of marine organisms is important for ecological research, biodiversity monitoring, habitat conservation, and evidence-based policy-making. However, many existing approaches primarily rely on object- or ROI-centered…

Computer Vision and Pattern Recognition · Computer Science 2026-05-29 Donghwan Lee , Byeongjin Kim , Geunhee Kim , Hyukjin Kwon , Nahyeon Maeng , Wooju Kim

Multimodal learning, a rapidly evolving field in artificial intelligence, seeks to construct more versatile and robust systems by integrating and analyzing diverse types of data, including text, images, audio, and video. Inspired by the…

Artificial Intelligence · Computer Science 2024-12-24 Priyaranjan Pattnayak , Hitesh Laxmichand Patel , Bhargava Kumar , Amit Agarwal , Ishan Banerjee , Srikant Panda , Tejaswini Kumar

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…

Computation and Language · Computer Science 2024-09-09 Jian Li , Weiheng Lu , Hao Fei , Meng Luo , Ming Dai , Min Xia , Yizhang Jin , Zhenye Gan , Ding Qi , Chaoyou Fu , Ying Tai , Wankou Yang , Yabiao Wang , Chengjie Wang

The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understand and generate multimodal content. While prior studies have…

Artificial Intelligence · Computer Science 2024-09-30 Lin Li , Guikun Chen , Hanrong Shi , Jun Xiao , Long Chen

With the rapid advancement of Multimodal Large Language Models (MLLMs), a variety of benchmarks have been introduced to evaluate their capabilities. While most evaluations have focused on complex tasks such as scientific comprehension and…

Computer Vision and Pattern Recognition · Computer Science 2024-12-24 Huan Liu , Lingyu Xiao , Jiangjiang Liu , Xiaofan Li , Ze Feng , Sen Yang , Jingdong Wang

We have witnessed promising progress led by large language models (LLMs) and further vision language models (VLMs) in handling various queries as a general-purpose assistant. VLMs, as a bridge to connect the visual world and language…

Computer Vision and Pattern Recognition · Computer Science 2025-12-25 YuK-Kwan Wong , Tuan-An To , Jipeng Zhang , Ziqiang Zheng , Sai-Kit Yeung

In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data…

Multimodal Large Language models (MLLMs) have shown promise in web-related tasks, but evaluating their performance in the web domain remains a challenge due to the lack of comprehensive benchmarks. Existing benchmarks are either designed…

Computation and Language · Computer Science 2024-04-10 Junpeng Liu , Yifan Song , Bill Yuchen Lin , Wai Lam , Graham Neubig , Yuanzhi Li , Xiang Yue

Multimodal language analysis is a rapidly evolving field that leverages multiple modalities to enhance the understanding of high-level semantics underlying human conversational utterances. Despite its significance, little research has…

Computation and Language · Computer Science 2025-04-25 Hanlei Zhang , Zhuohang Li , Yeshuang Zhu , Hua Xu , Peiwu Wang , Haige Zhu , Jie Zhou , Jinchao Zhang

The emergence of Multimodal Large Language Models (MLLMs) has revolutionized image understanding by bridging textual and visual modalities. However, these models often struggle with capturing fine-grained semantic information, such as the…

Computer Vision and Pattern Recognition · Computer Science 2025-07-16 Jie Yang , Wang Zeng , Sheng Jin , Lumin Xu , Wentao Liu , Chen Qian , Zhen Li , Ruimao Zhang

Recent advancements in Artificial Intelligence have led to the development of Multimodal Large Language Models (MLLMs). However, adapting these pre-trained models to dynamic data distributions and various tasks efficiently remains a…

Machine Learning · Computer Science 2025-03-05 Yukang Huo , Hao Tang

Multimodal Large Language Models (MLLMs) have made significant advancements, demonstrating powerful capabilities in processing and understanding multimodal data. Fine-tuning MLLMs with Federated Learning (FL) allows for expanding the…

Machine Learning · Computer Science 2025-03-11 Binqian Xu , Xiangbo Shu , Haiyang Mei , Guosen Xie , Basura Fernando , Jinhui Tang

Since the release of ChatGPT, the field of Natural Language Processing has experienced rapid advancements, particularly in Large Language Models (LLMs) and their multimodal counterparts, Large Multimodal Models (LMMs). Despite their…

Computation and Language · Computer Science 2024-08-27 Florian Schneider , Sunayana Sitaram

This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational…

Artificial Intelligence · Computer Science 2025-12-02 Chia Xin Liang , Pu Tian , Caitlyn Heqi Yin , Yao Yua , Wei An-Hou , Li Ming , Xinyuan Song , Tianyang Wang , Ziqian Bi , Ming Liu

Recently, the remarkable success of large language models (LLMs) has achieved a profound impact on the field of artificial intelligence. Numerous advanced works based on LLMs have been proposed and applied in various scenarios. Among them,…

Computer Vision and Pattern Recognition · Computer Science 2025-11-19 Xizhe Xue , Yang Zhou , Dawei Yan , Lijie Tao , Junjie Li , Ying Li , Haokui Zhang , Rong Xiao

Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to…

Computer Vision and Pattern Recognition · Computer Science 2025-10-27 Chaoyou Fu , Peixian Chen , Yunhang Shen , Yulei Qin , Mengdan Zhang , Xu Lin , Jinrui Yang , Xiawu Zheng , Ke Li , Xing Sun , Yunsheng Wu , Rongrong Ji , Caifeng Shan , Ran He
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