Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Jinguo Zhu, Weiyun Wang, Zhe Chen +44 authors
InternVL3 is a multimodal pre-trained language model that jointly learns from both multimodal data and text, improving performance and scalability through advanced techniques and setting a new state-of-the-art in multimodal tasks.
Ziyang Ma, Zhikang Niu, Wenming Tu +30 authors
AuK is an open-source foundational model that unifies speech generation and editing via natural-language instructions and audio context, using a multimodal language model, joint VAE, hybrid rectified-flow Transformer, and efficient distillation for fast inference.
Zhe Chen, Weiyun Wang, Yue Cao +37 authors
InternVL 2.5, an advanced multimodal large language model, showcases competitive performance across various benchmarks, including multimodal reasoning and understanding, and is the first open-source model to surpass 70% on the MMMU benchmark using Chain-of-Thought reasoning.
Mingyu Ouyang, Siyuan Hu, Kevin Qinghong Lin +2 authors
GameWorld presents a standardized benchmark for evaluating multimodal large language model agents in video games, featuring diverse games and verified metrics for comprehensive assessment.
Hyungjoo Chae, Sunghwan Kim, Junhee Cho +18 authors
The paper introduces Web-Shepherd, a process reward model for web navigation, which improves accuracy and cost-effectiveness in step-level trajectory assessment compared to existing multimodal large language models.
Yadong Li, Haoze Sun, Mingan Lin +24 authors
Baichuan-Omni, a 7B open-source Multimodal Large Language Model, excels in processing image, video, audio, and text, showcasing competitive performance across multimodal benchmarks.
Zichen Wen, Boxue Yang, Shuang Chen +31 authors
Innovator-VL demonstrates that principled training design and transparent methodology can achieve strong scientific intelligence with reduced data requirements while maintaining general vision performance.
Hao Fei, Yuan Zhou, Juncheng Li +29 authors
A General-Level evaluation framework is introduced to assess multimodal generalist performance in LLMs, using Synergy to measure consistent capabilities across comprehension and generation.
Cong Wei, Quande Liu, Zixuan Ye +5 authors
UniVideo, a dual-stream framework combining a Multimodal Large Language Model and a Multimodal DiT, extends unified modeling to video generation and editing, achieving state-of-the-art performance and supporting task composition and generalization.
Zichen Liu, Yue Yu, Hao Ouyang +6 authors
MagicQuill integrates an interface with a multimodal large language model and diffusion prior to enable efficient and precise real-time image editing through minimal user input.
Bingquan Dai, Li Ray Luo, Qihong Tang +9 authors
MeshCoder reconstructs complex 3D objects from point clouds into editable Blender Python scripts, enhancing shape-to-code reconstruction and 3D shape understanding through a multimodal large language model.
Zhe Chen, Weiyun Wang, Hao Tian +24 authors
InternVL 1.5, an open-source multimodal language model, improves visual understanding and high-resolution image processing through continuous learning, dynamic tile division, and a high-quality bilingual dataset, achieving state-of-the-art results in several benchmarks.
Qian Chen, Yafeng Chen, Yanni Chen +33 authors
MinMo, a multimodal large language model, integrates speech and text processing to achieve state-of-the-art performance in voice comprehension and generation, while enabling full-duplex conversation and instruction-following capabilities.
Chaoyou Fu, Haojia Lin, Zuwei Long +12 authors
VITA, an open-source Multimodal Large Language Model, excels in processing Video, Image, Text, and Audio with seamless interaction, showcasing advancements in multimodal understanding and human-computer interaction.
Jianzong Wu, Chao Tang, Jingbo Wang +3 authors
DiffSensei, a diffusion-based framework with a multimodal large language model, enhances manga generation by precisely controlling multi-character appearances and interactions using masked cross-attention and text cues.
Zhiliang Peng, Wenhui Wang, Li Dong +4 authors
Kosmos-2, a Multimodal Large Language Model, combines text-to-visual grounding with existing MLLM capabilities, enabling tasks like referring expression comprehension and phrase grounding.
Chuofan Ma, Yi Jiang, Jiannan Wu +2 authors
Groma, a Multimodal Large Language Model, utilizes region-level visual tokenization to enhance visual understanding and localization accuracy in image-grounded tasks.
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