Though dataset distillation has witnessed rapid development in recent years,
the distillation of multimodal data, e.g., image-text pairs, poses unique and
under-explored challenges. Unlike unimodal data, image-text contrastive
learning (ITC) data lack inherent categorization and should instead place
greater emphasis on modality correspondence. In this work, we propose Low-Rank
Similarity Mining (LoRS) for multimodal dataset distillation, that concurrently
distills a ground truth similarity matrix with image-text pairs, and leverages
low-rank factorization for efficiency and scalability. The proposed approach
brings significant improvement to the existing algorithms, marking a
significant contribution to the field of visual-language dataset distillation.
We advocate adopting LoRS as a foundational synthetic data setup for image-text
dataset distillation. Our code is available at
this https URL

提出了一种用于多模态数据集精简的低秩相似度挖掘（LoRS）方法，通过同时挖掘图像 - 文本对中的准确相似度矩阵，并利用低秩因式分解实现高效和可伸缩性，在现有算法上取得了显著的改进，对于视觉语言数据集精简领域做出了重要贡献。