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arXiv · 2312.08846

TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training

Chaoya Jiang·Wei ye·Haiyang Xu·Qinghao Ye·Ming Yan·Ji Zhang·Shikun Zhang
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Abstract

Self-supervised Multi-modal Contrastive Learning (SMCL) remarkably advances modern Vision-Language Pre-training (VLP) models by aligning visual and linguistic modalities. Due to noises in web-harvested text-image pairs, however, scaling up training data volume in SMCL presents considerable obstacles in terms of computational cost and data inefficiency. To improve data efficiency in VLP, we propose Text-aware Image Mixing (TiMix), which integrates mix-based data augmentation techniques into SMCL, yielding significant performance improvements without significantly increasing computational overhead. We provide a theoretical analysis of TiMixfrom a mutual information (MI) perspective, showing that mixed data samples for cross-modal contrastive learning implicitly serve as a regularizer for the contrastive loss. The experimental results demonstrate that TiMix exhibits a comparable performance on downstream tasks, even with a reduced amount of training data and shorter training time, when benchmarked against existing methods. This work empirically and theoretically demonstrates the potential of data mixing for data-efficient and computationally viable VLP, benefiting broader VLP model adoption in practical scenarios.

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Chaoya Jiang, Wei ye, Haiyang Xu, Qinghao Ye, Ming Yan, Ji Zhang, Shikun Zhang. 2024-02-24. TiMix: Text-aware Image Mixing for Effective Vision-Language Pre-training. https://arxiv.org/abs/2312.08846

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