Related papers: Low-Rank Similarity Mining for Multimodal Dataset Distillation

Low-Rank Similarity Mining for Multimodal Dataset Distillation

URL: http://arxiv.org/abs/2406.03793v1
Date: Thu, 6 Jun 2024 07:05:20 GMT
Title: Low-Rank Similarity Mining for Multimodal Dataset Distillation
Authors: Yue Xu, Zhilin Lin, Yusong Qiu, Cewu Lu, Yong-Lu Li,
Abstract summary: We propose Low-Rank Similarity Mining (LoRS) for multimodal dataset distillation. LoRS distills a ground truth similarity matrix with image-text pairs, and leverages low-rank factorization for efficiency and scalability.
Score: 50.45577048854653
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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