5 citations · 11 across the 13 of their papers we have counts for
13 papers · 1 filter
CultureVidBench: Benchmarking Cultural Understanding in Text-to-Video Generation
Xianjing Han, Yuhan Su, Yang Deng +3
Text-to-video (T2V) generation models have advanced rapidly, yet their ability to represent diverse cultural contexts remains underexplored. Existing benchmarks mainly focus on per…
ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
Bin Zhu, Yanhao Jia, Kexin Zhao +12
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. However, a critical ambiguity pe…
OSP-Next: Efficient High-Quality Video Generation with Sparse Sequence Parallelism, HiF8 Quantization, and Reinforcement Learning
Yunyang Ge, Xianyi He, Zezhong Zhang +4
Diffusion Transformers achieve strong video generation quality, but the quadratic cost of full attention limits efficiency. We introduce OSP-Next, an efficient text-to-video genera…
Malicious Image Analysis via Vision-Language Segmentation Fusion: Detection, Element, and Location in One-shot
Sheng Hang, Chaoxiang He, Hongsheng Hu +5
Detecting illicit visual content demands more than image-level NSFW flags; moderators must also know what objects make an image illegal and where those objects occur. We introduce…
Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
Yuwei Niu, Weiyang Jin, Jiaqi Liao +7
Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation? To investigate this,…
A benchmark multimodal oro-dental dataset for large vision-language models
Haoxin Lv, Ijazul Haq, Jin Du +7
The advancement of artificial intelligence in oral healthcare relies on the availability of large-scale multimodal datasets that capture the complexity of clinical practice. In thi…