4 papers · 1 filter
VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision
Zhehan Kan, Yubo Zhu, Xinghua Jiang +10
While Vision-Language Models (VLMs) demonstrate strong capabilities, they continue to suffer from a critical limitation: insufficient fine-grained visual perception, which fundamen…
UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm
Zhehan Kan, Xinghua Jiang, Yubo Zhu +10
Despite remarkable advancements in multimodal large language models (MLLMs), their fine-grained visual understanding is constrained by a primary reliance on sparse textual supervis…
Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision
Zhixiang Wei, Yi Li, Zhehan Kan +38
Despite the significant advancements represented by Vision-Language Models (VLMs), current architectures often exhibit limitations in retaining fine-grained visual information, lea…
Youtu-Parsing: Perception, Structuring and Recognition via High-Parallelism Decoding
Haoyu Cao, Kun Yin, Yunfei Wu +16
This paper presents Youtu-Parsing, an efficient and versatile document parsing model designed for high-performance content extraction. The architecture employs a native Vision Tran…