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。
Dawei Zhu, Rui Meng, Yale Song +4 authors
_paperbanana is an agentic framework that automates the creation of publication-ready academic illustrations using advanced vision-language models and image generation techniques.
Andrés Marafioti, Orr Zohar, Miquel Farré +14 authors
SmolVLM, a series of compact multimodal models, achieves high performance with minimal GPU memory usage, making efficient deployment on mobile and edge devices possible.
Yiying Yang, Wei Cheng, Sijin Chen +6 authors
OmniSVG, using pre-trained Vision-Language Models and discrete tokenization, generates high-quality SVGs efficiently with a large, multimodal dataset.
Daxiang Dong, Mingming Zheng, Dong Xu +17 authors
Qianfan-OCR is a 4B-parameter vision-language model that unifies document parsing, layout analysis, and understanding while maintaining strong performance across multiple OCR benchmarks through its Layout-as-Thought mechanism.
Hugo Laurençon, Andrés Marafioti, Victor Sanh +1 authors
A comprehensive tutorial on building vision-language models, detailing the development of Idefics3-8B with an improved dataset enhancing document understanding.
Matt Deitke, Christopher Clark, Sangho Lee +48 authors
Molmo, a new family of open-weight VLMs, excels through a high-quality human-annotated image caption dataset and a diverse fine-tuning dataset, outperforming both proprietary and open models in benchmark and human evaluations.
Wenqi Zhang, Hang Zhang, Xin Li +6 authors
A new high-quality instructional video-based corpus improves VLM pretraining through more coherent context and richer knowledge, enhancing performance in knowledge-intensive tasks.
Kevin Qinghong Lin, Yuhao Zheng, Hangyu Ran +5 authors
VCode introduces a benchmark for generating SVG code from images to preserve symbolic meaning, highlighting gaps in visual-centric coding and proposing VCoder to improve performance.
Haoran Wei, Yaofeng Sun, Yukun Li
DeepSeek-OCR uses optical 2D mapping to compress long contexts, achieving high OCR precision with reduced vision tokens and demonstrating practical value in document processing.
Yuanyuan Gao, Hao Li, Yifei Liu +14 authors
Holi-Spatial presents the first fully automated, large-scale, spatially-aware multimodal dataset constructed from raw video inputs, supporting multi-level spatial supervision for 3D scene understanding and spatial reasoning tasks.
Pooyan Rahmanzadehgervi, Logan Bolton, Mohammad Reza Taesiri +1 authors
State-of-the-art large language models with vision capabilities perform poorly on simple visual tasks, indicating limitations in their visual understanding.
Ujjwal Upadhyay, Mukul Ranjan, Zhiqiang Shen +1 authors
SpookyBench is a benchmark for temporal pattern recognition in videos that highlights the limitations of vision-language models in processing noise-like frames without spatial information.
Qiushi Sun, Mukai Li, Zhoumianze Liu +11 authors
A hybrid safety detection framework combining formal verification and VLM-based contextual assessment improves the detection of unsafe operations in mobile agents.
Zeyi Sun, Ziyang Chu, Pan Zhang +6 authors
X-Prompt, an auto-regressive large-vision language model, uses in-context learning to achieve competitive performance in seen and unseen image generation tasks.
Hugo Laurençon, Léo Tronchon, Victor Sanh
A synthetic dataset of HTML code and screenshots is introduced to enhance the ability of vision-language models to convert screenshots into HTML code.
Alejandro Lozano, Min Woo Sun, James Burgess +13 authors
BIOMEDICA is a scalable framework that creates a large, publicly accessible dataset of image-text pairs from PubMed Central, enabling state-of-the-art performance across various biomedical tasks with efficient CLIP-style models.
Haiwen Diao, Yufeng Cui, Xiaotong Li +3 authors
EVE is an encoder-free vision-language model that achieves competitive performance on multiple benchmarks using a unified decoder and extra supervision.
Yanwei Li, Yuechen Zhang, Chengyao Wang +5 authors
Mini-Gemini enhances multi-modality Vision Language Models through high-resolution visual tokens, high-quality datasets, and VLM-guided generation, achieving leading performance in zero-shot benchmarks.
Yining Hong, Haoyu Zhen, Peihao Chen +4 authors
A new family of 3D-LLMs is introduced to perform 3D-related tasks by leveraging 3D point clouds and features, outperforming state-of-the-art baselines in tasks such as 3D question answering and captioning.
Jingkang Yang, Yuhao Dong, Shuai Liu +8 authors
Octopus, a novel vision-language model, integrates multimodal perception and reasoning to perform diverse tasks using GPT-4 and Reinforcement Learning with Environmental Feedback in an experimental environment called OctoVerse.
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