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。
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.
Xianjin Wu, Dingkang Liang, Tianrui Feng +5 authors
A video diffusion model is repurposed as a latent world simulator to enhance multimodal large language models with implicit 3D structural priors and physical laws through spatiotemporal feature extraction and semantic integration.
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.
Shijian Wang, Jiarui Jin, Xingjian Wang +6 authors
Video-Thinker, a multimodal large language model, autonomously reasons with videos using intrinsic grounding and captioning capabilities, achieving state-of-the-art performance on various video reasoning 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.
Runhui Huang, Qihui Zhang, Zhe Liu +3 authors
SpectraReward leverages pretrained multimodal language models as training-free reward models for image-generation reinforcement learning by measuring prompt recovery likelihood, with a self-improving variant that uses the policy's own understanding branch to close the loop.
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.
Gagan Bhatia, El Moatez Billah Nagoudi, Hasan Cavusoglu +1 authors
FinTral, a multimodal LLM enhanced through domain-specific pretraining, instruction fine-tuning, and RLAIF, outperforms ChatGPT-3.5 and GPT-4 in financial analysis tasks with exceptional zero-shot performance.
Jiakang Yuan, Tianshuo Peng, Yilei Jiang +8 authors
MME-Reasoning evaluates the logical reasoning capabilities of multimodal large language models, revealing significant limitations and performance imbalances across inductive, deductive, and abductive reasoning types.
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.
Jiaqi Tang, Jianmin Chen, Youyang Zhai +6 authors
Robust-U1 enhances multimodal large language models' robustness against visual corruptions through self-recovery capabilities that improve both visual quality and reasoning performance.
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.
Artem Chervyakov, Ulyana Isaeva, Anton Emelyanov +15 authors
Mera Multi is an open multimodal evaluation framework for Russian-spoken architectures, addressing the lack of such benchmarks with 18 newly constructed tasks and a methodology to prevent benchmark leakage.
Shengqiong Wu, Hao Fei, Leigang Qu +2 authors
NExT-GPT, an any-to-any Multimodal Large Language Model, combines LLMs with multimodal adaptors and diffusion decoders to generate content across various modalities, enhanced by modality-switching instruction tuning and a curated dataset.
Weiye Xu, Jiahao Wang, Weiyun Wang +10 authors
VisuLogic is a benchmark to evaluate genuine vision-centric reasoning in multimodal large language models, revealing significant performance gaps compared to human accuracy.
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.
Wenliang Dai, Nayeon Lee, Boxin Wang +7 authors
NVML 1.0, a family of multimodal large language models, achieves state-of-the-art results in vision-language tasks by combining text-only and multimodal training, utilizing a new architecture and dataset strategy.
Yuhao Zhou, Yiheng Wang, Xuming He +24 authors
Scientists' First Exam (SFE) benchmark assesses scientific cognitive capacities of Multimodal Large Language Models through perception, understanding, and comparative reasoning.
Haowei Zhang, Shudong Yang, Jinlan Fu +2 authors
HERMES is a training-free architecture that enables real-time video stream understanding by utilizing a hierarchical memory framework based on KV cache reuse, achieving faster response times and maintained accuracy even with reduced video token input.
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