用多模态模型动态筛选数据，提升训练效率与模型鲁棒性
Multimodal-Guided Dynamic Dataset Pruning for Robust and Efficient Data-Centric Learning
- 基于任务难度和跨模态一致性动态选择样本
- 相比静态方法，提升模型在不同领域的泛化能力
- 适合需要高效高质量数据的视觉-语言任务
现代深度模型通常在大规模真实数据集上训练，数据质量参差不齐且存在冗余。以数据集剪枝为代表的数据中心范式在提升训练效率和模型性能方面展现出潜力。然而，现有方法大多依赖静态启发式或任务特定指标，限制了其在不同领域中的鲁棒性和通用性。本文提出一种动态数据剪枝框架，根据任务驱动的难易度与跨模态语义一致性自适应选择训练样本。通过引入预训练多模态基础模型的监督信号，该方法能够捕捉训练动态，并有效过滤无信息样本。实验表明，该方法在多个数据集上显著提升了模型性能与训练效率，展示了跨模态对齐在稳健样本选择中的巨大潜力，推动数据中心学习向更高效、更鲁棒的方向发展。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。