Results 311 to 320 of about 1,322,987 (378)

InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

arXiv.org
We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model (LLM) into a multimodal large language model (MLLM) that supports visual ...
Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Yuchen Duan, Hao Tian, Weijie Su, Jie Shao, Zhangwei Gao, Erfei Cui, Yue Cao, Yangzhou Liu, Haomin Wang, Weiye Xu, Hao Li, Jiahao Wang, Han Lv, De-Hua Chen, Songze Li, Yinan He, Tan Jiang, Jiapeng Luo, Yi Wang, Conghui He, Botian Shi, Xingcheng Zhang, Wenqi Shao, Junjun He, Ying Xiong, Wenwen Qu, Peng Sun, Penglong Jiao, Lijun Wu, Kai Zhang, Hui Deng, Jiaye Ge, Kaiming Chen, Limin Wang, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, Wenhai Wang   +47 more
semanticscholar   +1 more source

Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

arXiv.org
We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data ...
Zhe Chen, Weiyun Wang, Yue Cao, Yangzhou Liu, Zhangwei Gao, Erfei Cui, Jinguo Zhu, Shenglong Ye, Hao Tian, Zhaoyang Liu, Lixin Gu, Xuehui Wang, QingYun Li, Yiming Ren, Zixuan Chen, Jiapeng Luo, Jiahao Wang, Tan Jiang, Bo Wang, Conghui He, Botian Shi, Xingcheng Zhang, Han Lv, Yi Wang, Wenqi Shao, Pei Chu, Zhongying Tu, Tong He, Zhiyong Wu, Hui Deng, Jiaye Ge, Kaiming Chen, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahu Lin, Yunfeng Qiao, Jifeng Dai, Wenhai Wang   +39 more
semanticscholar   +1 more source

Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling

arXiv.org
In this work, we introduce Janus-Pro, an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strategy, (2) expanded training data, and (3) scaling to larger model size.
Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, C. Ruan   +7 more
semanticscholar   +1 more source

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

arXiv.org
We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the Cascade Reinforcement Learning (Cascade RL)
Weiyun Wang, Zhangwei Gao, Lixin Gu, Hengjun Pu, Long Cui, Xingguang Wei, Zhaoyang Liu, Linglin Jing, Shenglong Ye, Jie Shao, Zhaokai Wang, Zhe Chen, Hongjie Zhang, Ganlin Yang, Haomin Wang, Qi Wei, Jinhui Yin, Wenhao Li, Erfei Cui, Guanzhou Chen, Zichen Ding, Changyao Tian, Zhenyu Wu, Jingjing Xie, Zehao Li, Bowen Yang, Yuchen Duan, Xuehui Wang, Haoran Hao, Songze Li, Xiangyu Zhao, Haodong Duan, Nianchen Deng, Bin Fu, Yinan He, Yi Wang, Conghui He, Botian Shi, Junjun He, Ying Xiong, Han Lv, Lijun Wu, Wenqi Shao, Kai Zhang, Hui Deng, Biqing Qi, Biqing Qi, Qipeng Guo, Wenwei Zhang, Yuzhe Gu, Wanli Ouyang, Limin Wang, Min Dou, Xizhou Zhu, Tong Lu, Dahua Lin, Jifeng Dai, Bowen Zhou, Weijie Su, Kaiming Chen, Yu Qiao, Wenhai Wang, Gen Luo   +62 more
semanticscholar   +1 more source

Generating Multimodal Grammars for Multimodal Dialogue Processing

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2010
This paper presents a new multimodal grammar generation system (MGGS) that allows defining a multimodal grammar in a very easy and intuitive way, overcoming the difficulties arising from the textual description of grammar production rules. The novelty of the proposed approach relies in adopting a by example paradigm to define a multimodal grammar. This
D'Ulizia Arianna, Ferri Fernando, Grifoni Patrizia   +2 more
openaire   +3 more sources

Emerging Properties in Unified Multimodal Pretraining

arXiv.org
Unifying multimodal understanding and generation has shown impressive capabilities in cutting-edge proprietary systems. In this work, we introduce BAGEL, an open-source foundational model that natively supports multimodal understanding and generation ...
Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiao-Ping Nie, Ziang Song, Shi Guang, Haoqi Fan   +11 more
semanticscholar   +1 more source

Multimodal Unsupervised Image-to-Image Translation

European Conference on Computer Vision, 2018
Unsupervised image-to-image translation is an important and challenging problem in computer vision. Given an image in the source domain, the goal is to learn the conditional distribution of corresponding images in the target domain, without seeing any ...
Xun Huang, Ming-Yu Liu, Serge J. Belongie, Jan Kautz   +3 more
semanticscholar   +1 more source

Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

arXiv.org
We introduce Phi-4-Mini and Phi-4-Multimodal, compact yet highly capable language and multimodal models. Phi-4-Mini is a 3.8-billion-parameter language model trained on high-quality web and synthetic data, significantly outperforming recent open-source ...
Abdelrahman Abouelenin, Atabak Ashfaq, Adam Atkinson, H. Awadalla, Nguyen Bach, Jianmin Bao, Alon Benhaim, Martin Cai, Vishrav Chaudhary, Congcong Chen, Dongdong Chen, Dongdong Chen, Junkun Chen, Weizhu Chen, Yen-Chun Chen, Yi-Ling Chen, Qi Dai, Xiyang Dai, Ruchao Fan, Mei Gao, Mingcheng Gao, Amit Garg, Abhishek Goswami, Junheng Hao, Amr Hendy, Yuxuan Hu, Xin Jin, Mahmoud Khademi, Dongwoo Kim, Young Jin Kim, Gina Lee, Jinyu Li, Yunsheng Li, Chen Liang, Xihui Lin, Zeqi Lin, Meng-Jie Liu, Yang Liu, Gilsinia Lopez, Chong Luo, Piyush Madan, V. Mazalov, Ali Mousavi, A. Nguyen, Jing Pan, D. Perez-Becker, Jacob Platin, Thomas Portet, Kai Qiu, Bo Ren, Liliang Ren, Sambuddha Roy, Ning Shang, Yelong Shen, Saksham Singhal, Subhojit Som, Xiaocheng Song, T. Sych, Praneetha Vaddamanu, Shuohang Wang, Yiming Wang, Zhenghao Wang, Haibin Wu, Haoran Xu, Weijian Xu, Yifan Yang, Ziyi Yang, Donghan Yu, I. Zabir, Jianwen Zhang, L. Zhang, Yunan Zhang, Xiren Zhou   +72 more
semanticscholar   +1 more source

Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models

arXiv.org
DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by this breakthrough, we explore how RL can be utilized to enhance the reasoning capability of MLLMs ...
Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaoshen Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, Shaohui Lin   +8 more
semanticscholar   +1 more source

How far are we to GPT-4V? Closing the gap to commercial multimodal models with open-source suites

Science China Information Sciences
In this paper, we introduce InternVL 1.5, an open-source multimodal large language model (MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.
Zhe Chen, Weiyun Wang, Hao Tian, Shenglong Ye, Zhangwei Gao, Erfei Cui, Wenwen Tong, Kongzhi Hu, Jiapeng Luo, Zheng Ma, Ji Ma, Jiaqi Wang, Xiao-wen Dong, Hang Yan, Hewei Guo, Conghui He, Zhenjiang Jin, Chaochao Xu, Bin Wang, Xingjian Wei, Wei Li, Wenjian Zhang, Bo Zhang, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao   +27 more
semanticscholar   +1 more source