Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning

• URL: http://arxiv.org/abs/2507.12750v1
• Date: Thu, 17 Jul 2025 03:08:26 GMT
• Title: Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning
• Authors: Suorong Yang, Peijia Li, Yujie Liu, Zhiming Xu, Peng Ye, Wanli Ouyang, Furao Shen, Dongzhan Zhou,
• Abstract summary: We introduce a dynamic dataset pruning framework that adaptively selects training samples based on task-driven difficulty and cross-modality semantic consistency.<n>Our work highlights the potential of integrating cross-modality alignment for robust sample selection, advancing data-centric learning toward more efficient and robust practices across application domains.
• Score: 49.10890099624699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern deep models are trained on large real-world datasets, where data quality varies and redundancy is common. Data-centric approaches such as dataset pruning have shown promise in improving training efficiency and model performance. However, most existing methods rely on static heuristics or task-specific metrics, limiting their robustness and generalizability across domains. In this work, we introduce a dynamic dataset pruning framework that adaptively selects training samples based on both task-driven difficulty and cross-modality semantic consistency. By incorporating supervision from pretrained multimodal foundation models, our approach captures training dynamics while effectively filtering out uninformative samples. Our work highlights the potential of integrating cross-modality alignment for robust sample selection, advancing data-centric learning toward more efficient and robust practices across application domains.

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