2 papers
cs.CV2026
DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models
Biao Wu, Yiwu Zhong, Meng Fang +1
High-quality and diverse multimodal data are essential for improving vision-language models (VLMs), yet existing datasets often contain noisy, redundant, and poorly aligned samples…
cs.CV2024
TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
Mu Cai, Reuben Tan, Jianrui Zhang +12
Understanding fine-grained temporal dynamics is crucial for multimodal video comprehension and generation. Due to the lack of fine-grained temporal annotations, existing video benc…