mlsanityData to 13 Oct 2026

Low-Rank Similarity Mining for Multimodal Dataset Distillation

arXiv 2406.03793 ↗First posted 6 Jun 2024cs.LG cs.CVcited by 16

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Abstract (arXiv)

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 https://github.com/silicx/LoRS_Distill.

Key facts. Cited by 16 papers in the map (data through 13 Oct 2026). Builds on 49 of its 68 references that are in the map. First posted on arXiv 6 Jun 2024.

Builds on

49 of 68 references.

References · 49

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 6.

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