Author

Shi-Feng Liu

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Preprint Aug 2026

VIVAS: Vitalizing Visual Perception in VLM Pre-training via Vision-language Unified Autoregressive Supervision

While Vision-Language Models (VLMs) demonstrate strong capabilities, they continue to suffer from a critical limitation: insufficient fine-grained visual perception, which fundamentally limits their multimodal understanding. We attribute this bottleneck to text-dominant optimization biases during pre-training, which en...

Zhe-Han Kan, Yu-Bo Zhu, Xing-Hua Jiang et al. · 0 citations

Preprint Aug 2026

UVU: Improving Multimodal Understanding via Vision-Language Unified Autoregressive Paradigm

Despite remarkable advancements in multimodal large language models (MLLMs), their fine-grained visual understanding is constrained by a primary reliance on sparse textual supervision. Existing efforts to introduce visual supervision typically do so during post-training, when visual representations have already been la...

Zhe-Han Kan, Xing-Hua Jiang, Yu-Bo Zhu et al. · 0 citations