Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target short videos, extending them to long videos remains challenging, primarily due to limited context windows of current video understanding models. A naive solution is to...
Xinlong Chen, Wei-Hong Lin, Yuan-Xing Zhang et al.· 0 citations
Dense visual text requires image generators to reproduce long strings across multiple regions with correct placement and legibility. As short-string rendering improves, evaluation must test sustained performance across more demanding scenes. We introduce UltraText Bench, a bilingual benchmark for prompt-only generation...
De-Yuan Liu, Yihao Hu, Jing-Xuan Zhang et al.· 0 citations
This work introduces SPACE (Subject-centric Poses, Appearance, and Characteristics Evaluation), a benchmark designed to evaluate subject-centered spatial understanding, and designs a rubric-based reward for Group Relative Policy Optimization that explicitly penalizes structurally critical spatial errors during alignmen...
Bozhou Li, Jia-Hang Zhang, Yue Ding et al.· 0 citations
Human evaluation confirms that RefCaptioner achieves the best overall performance among the open-source models while remaining competitive on standard video captioning benchmarks, and human evaluation further confirms that its captions are preferred by annotators and enable more source-faithful video reconstruction wit...
Teng-Fei Liu, Yang Shi, Yu-Ran Wang et al.· arXiv.org· 0 citations
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