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。
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.
Hongcheng Gao, Zihao Huang, Lin Xu +11 authors
The Turing Eye Test evaluates MLLMs' perceptual capabilities on synthetic images, revealing that vision tower generalization is a key gap compared to human perception.
Jiaqi Tang, Jianmin Chen, Wei Wei +7 authors
A novel framework, Robust-R1, enhances multimodal large language models' robustness to visual degradations through explicit modeling, supervised fine-tuning, reward-driven alignment, and dynamic reasoning depth scaling, achieving state-of-the-art performance on real-world degradation benchmarks.
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.
Jiuhai Chen, Jianwei Yang, Haiping Wu +4 authors
Florence-VL enhances multimodal language models with improved visual features from Florence-2, achieving state-of-the-art results across various benchmarks through depth-breath fusion and a novel training recipe.
Zhengxi Lu, Yuxiang Chai, Yaxuan Guo +5 authors
Rule-based reinforcement learning enhances multimodal large language models for GUI action prediction, improving data efficiency and performance.
Tengchao Lv, Yupan Huang, Jingye Chen +11 authors
Kosmos-2.5, a unified multimodal model, generates spatially-aware and structured text from text-intensive images using a Transformer architecture and task-specific prompts.
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.
Jiarui Zhang, Ollie Liu, Tianyu Yu +2 authors
A benchmark called Geoperception is introduced to evaluate MLLMs' geometric description accuracy, leading to the development of Euclid, a model optimized for low-level geometric perception using synthetic data and a data curriculum.
Haotian Zhang, Mingfei Gao, Zhe Gan +20 authors
MM1.5 enhances multimodal language models with a data-centric approach, supporting diverse tasks like text-rich image understanding and video comprehension through careful data curation and training strategies.
Zekun Wang, King Zhu, Chunpu Xu +14 authors
MIO, a novel foundation model, achieves competitive performance in multimodal tasks through an end-to-end, autoregressive approach using causal multimodal modeling.
NVIDIA, Alisson Azzolini, Hannah Brandon +42 authors
Cosmos-Reason1 models, using hierarchical and two-dimensional ontologies for physical common sense and embodied reasoning, generate embodied decisions through multimodal large language models trained in vision and Physical AI stages.
Dingjie Song, Sicheng Lai, Shunian Chen +2 authors
A framework called MM-Detect is introduced to detect data contamination in multimodal large language models, demonstrating sensitivity across different contamination levels and identifying potential contamination stages during pre-training and fine-tuning.
Duzhen Zhang, Yahan Yu, Chenxing Li +4 authors
A survey examining the design, performance, and future directions of MultiModal Large Language Models (MM-LLMs) to enhance reasoning and MM task capabilities.
Run Luo, Haonan Zhang, Longze Chen +13 authors
MMEvol, a multimodal instruction data evolution framework, enhances the capabilities of Multimodal Large Language Models by generating diverse and complex image-text instruction datasets, leading to improved performance across various vision-language tasks.
Xudong Lu, Yinghao Chen, Cheng Chen +19 authors
BlueLM-V-3B optimizes the deployment of multimodal large language models on mobile platforms through redesigned dynamic resolution and hardware-aware system optimizations, achieving high performance with a small model size and fast generation speed.
Jarvis Guo, Tuney Zheng, Yuelin Bai +7 authors
A large-scale multimodal instruction-tuning dataset with intermediate rationales improves reasoning capabilities in large language models, achieving state-of-the-art performance across benchmarks.
Ting Jiang, Minghui Song, Zihan Zhang +6 authors
E5-V, a novel framework, utilizes multimodal large language models (MLLMs) to achieve universal multimodal embeddings with strong performance and reduced costs through single modality training.
Yifan Shen, Jian Xu, Boyi Li +6 authors
ChronoVision improves visual temporal reasoning by aligning latent imagery with logic through reconstructive prediction, ROI attention, and reinforcement learning, achieving strong results on video reasoning benchmarks.
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