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。
Shuo Liang, Yixing Ma, Pengfei Zhou +32 authors
A new benchmark for AI-generated video detection reveals that current detectors fail to generalize across realistic crisis-related videos and become less reliable as content spreads socially.
Junxiang Xu, Ruisi Wang, Fanyi Pu +49 authors
VBBR-Pro introduces a closed-loop testbed that enables scalable, verifiable, and controllable native visual reasoning through generation across diverse visual substrates.
Runqi Qiao, Qiuna Tan, Peiqing Yang +11 authors
We-Math 2.0 enhances MLLMs' mathematical reasoning through a structured knowledge system, model-centric data space modeling, and reinforcement learning, demonstrating competitive performance on benchmarks.
Shiyin Lu, Yang Li, Yu Xia +39 authors
Ovis2.5, a native-resolution vision transformer with multimodal reasoning, achieves state-of-the-art performance on various benchmarks through advanced training techniques and efficient scaling methods.
LASA Team, Weiwen Xu, Hou Pong Chan +16 authors
A medical-specialized multimodal large language model, Lingshu, is introduced with enhanced data curation and reinforcement learning to address limitations in medical applications.
Pan Zhang, Xiaoyi Dong, Yuhang Cao +26 authors
A framework combining disentangled perception, reasoning, and memory mechanisms enables real-time interaction for long-term multimodal AI systems.
Yuan Yao, Tianyu Yu, Ao Zhang +20 authors
MiniCPM-V presents a series of efficient Multimodal Large Language Models optimized for end-side deployment, offering high performance and practical usability compared to larger models.
Shijia Yang, Bohan Zhai, Quanzeng You +3 authors
Correlation between cross-modal alignment and vision representation improves performance in multimodal large language models, enabling identification and training of optimal vision representation with reduced computational cost.
Min Shi, Fuxiao Liu, Shihao Wang +12 authors
Mixture of vision encoders and resolutions in multimodal large language models improves performance through concatenation of visual tokens and a Pre-Alignment mechanism, leading to superior results on benchmarks.
Weiyun Wang, Zhe Chen, Wenhai Wang +8 authors
A preference optimization process enhances multimodal reasoning, particularly Chain-of-Thought performance, in large language models through automated data construction and mixed preference optimization, demonstrating significant improvements on benchmarks.
Heeji Yoon, Jaewoo Jung, Junwan Kim +10 authors
VIRAL, a regularization strategy, aligns MLLMs' visual representations with pre-trained VFMs, enhancing performance on vision-centric tasks.
Keen You, Haotian Zhang, Eldon Schoop +5 authors
Ferret-UI, a multimodal large language model tailored for mobile UI screens, enhances understanding and interaction through region annotations and a comprehensive dataset of UI tasks, outperforming existing models including GPT-4V.
Yi-Fan Zhang, Xingyu Lu, Shukang Yin +17 authors
Thyme, a novel paradigm, enables MLLMs to autonomously perform image manipulations and computations, enhancing performance in perception and reasoning tasks through a two-stage training strategy and GRPO-ATS algorithm.
Kaituo Feng, Kaixiong Gong, Bohao Li +5 authors
Video-R1, leveraging rule-based reinforcement learning and temporal information, enhances video reasoning in multimodal large language models using a combination of video and image data.
Shengqiong Wu, Weicai Ye, Jiahao Wang +8 authors
Any2Caption framework improves video generation quality and controllability by decoupling condition interpretation and leveraging multimodal large language models.
Renjie Pi, Jianshu Zhang, Tianyang Han +3 authors
A new framework called Personalized Visual Instruction Tuning (PVIT) enhances multimodal large language models to recognize and engage with specific individuals in images, utilizing a curated dataset and benchmarks for evaluation.
Junhao Cheng, Yuying Ge, Yixiao Ge +2 authors
AnimeGamer uses Multimodal Large Language Models (MLLMs) and a video diffusion model to generate contextually consistent, dynamic, and high-quality video clips for an infinite game simulation.
Junying Chen, Ruyi Ouyang, Anningzhe Gao +9 authors
The refinement of medical image-text pairs using multimodal large language models enhances medical multimodal capabilities and data quality, leading to superior performance in benchmarks.
Rongyao Fang, Chengqi Duan, Kun Wang +7 authors
PUMA, a unified multimodal large language model, addresses varying granularity demands in visual content generation tasks through the integration of multi-granular visual features.
Weiyun Wang, Shuibo Zhang, Yiming Ren +13 authors
NN-NIAH is a benchmark that evaluates MLLMs' comprehension of long multimodal documents across retrieval, counting, and reasoning tasks.
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