19 citations · 27 across the 7 of their papers we have counts for
10 papers · 1 filter
OmniCaptioner: One Captioner to Rule Them All
Yiting Lu, Jiakang Yuan, Zhen Li +17
We propose OmniCaptioner, a versatile visual captioning framework for generating fine-grained textual descriptions across a wide variety of visual domains. Unlike prior methods lim…
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Jinguo Zhu, Weiyun Wang, Zhe Chen +48
We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm. Rather than adapting a text-only large language model…
MM-Eureka: Exploring the Frontiers of Multimodal Reasoning with Rule-based Reinforcement Learning
Fanqing Meng, Lingxiao Du, Zongkai Liu +12
DeepSeek R1, and o1 have demonstrated powerful reasoning capabilities in the text domain through stable large-scale reinforcement learning. To enable broader applications, some wor…
GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training
Renqiu Xia, Mingsheng Li, Hancheng Ye +12
Despite their proficiency in general tasks, Multi-modal Large Language Models (MLLMs) struggle with automatic Geometry Problem Solving (GPS), which demands understanding diagrams,…
OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
Linke Ouyang, Yuan Qu, Hongbin Zhou +17
Document content extraction is a critical task in computer vision, underpinning the data needs of large language models (LLMs) and retrieval-augmented generation (RAG) systems. Des…
Chimera: Improving Generalist Model with Domain-Specific Experts
Tianshuo Peng, Mingsheng Li, Jiakang Yuan +11
Recent advancements in Large Multi-modal Models (LMMs) underscore the importance of scaling by increasing image-text paired data, achieving impressive performance on general tasks.…