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。
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.
Yansong Ning, Jingwen Ye, Zhongkai Wu +5 authors
A unified framework benchmarks and trains multimodal agents that infer intent, plan 3D scenes, invoke tools, and reflect on feedback, revealing that reinforcement learning improves open-source models beyond closed-source frontiers.
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.
Sijie Cheng, Kechen Fang, Yangyang Yu +6 authors
VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities, highlights the need for advancements in Multi-modal Large Language Models for effective application in Embodied AI.
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.
Chaochao Lu, Chen Qian, Guodong Zheng +33 authors
A qualitative study evaluates the generalizability, trustworthiness, and causal reasoning capabilities of MLLMs across text, code, image, and video modalities to understand their reliability for downstream applications.
Han Zhao, Min Zhang, Wei Zhao +3 authors
Cobra, a linear-complexity multimodal large language model, integrates the Mamba language model with visual modality, achieving competitive performance and faster inference speed compared to existing state-of-the-art models.
Zhengqing Yuan, Zhaoxu Li, Lichao Sun
TinyGPT-V, built on Phi-2 with vision modules from BLIP-2 or CLIP, offers high performance with low computational requirements, enabling efficient multimodal large language modeling.
En Yu, Liang Zhao, Yana Wei +8 authors
A novel MLLM, Merlin, is introduced which incorporates future modeling through FPT and FIT, enhancing foresight and future reasoning capabilities.
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