DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models
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. To address these problems, data filtering is commonly used to enhance the efficiency and performance of multimodal learning, but it introduces extra computational cost because filtering models are usually trained on the same data they are meant to screen. To reduce this cost, we study DOSE, which explores whether off-the-shelf pretrained models that have never seen the target data can be used to select training samples for larger and stronger multimodal models without any task-specific training. Even without f...
| Authors | Biao Wu, Yiwu Zhong, Meng Fang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 18 April 2026 |
| Last updated at source | 18 April 2026 |
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