Video generation begins in text space by authoring a cinematic screenplay, then materializes into pixels. As contemporary video generators scale to 30 seconds and faithfully follow complex conditions, the textual prompt largely directs the production, planning how actions, camera trajectories, lighting, and sound unfol...
Yu-Bo Zhu, Ya-Wen Shao, Zi-Yun Dai et al.· 0 citations
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
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
VGAU-Diag is introduced, a fine-grained evaluation framework for vision generation-assisted understanding that stratifies samples by difficulty, enables unified evaluation of multiple reasoning paradigms, and uses Oracle-Ass Reference Protocols.
Yu-Bo Zhu, Zhe-Han Kan, Jing-Yi Yang et al.· 1 citation
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