Too Large; Data Reduction for Vision-Language Pre-Training

Alex Jinpeng Wang, Kevin Qinghong Lin, David Junhao Zhang, Stan Weixian Lei, Mike Zheng Shou; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 3147-3157


This paper examines the problems of severe image-text misalignment and high redundancy in the widely-used large-scale Vision-Language Pre-Training (VLP) datasets. To address these issues, we propose an efficient and straightforward Vision-Language learning algorithm called TL;DR which aims to compress the existing large VLP data into a small, high-quality set. Our approach consists of two major steps. First, a codebook-based encoder-decoder captioner is developed to select representative samples. Second, a new caption is generated to complement the original captions for selected samples, mitigating the text-image misalignment problem while maintaining uniqueness. As the result, TL;DR enables us to reduce the large dataset into a small set of high-quality data, which can serve as an alternative pre-training dataset. This algorithm significantly speeds up the time-consuming pretraining process. Specifically, TL;DR can compress the mainstream VLP datasets at a high ratio, e.g., reduce well-cleaned CC3M dataset from 2.8M to 0.67M ( 24%) and noisy YFCC15M from 15M to 2.5M ( 16.7%). Extensive experiments with three popular VLP models over seven downstream tasks show that VLP model trained on the compressed dataset provided by TL;DR can perform similar or even better results compared with training on the full-scale dataset.

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[pdf] [arXiv]
@InProceedings{Wang_2023_ICCV, author = {Wang, Alex Jinpeng and Lin, Kevin Qinghong and Zhang, David Junhao and Lei, Stan Weixian and Shou, Mike Zheng}, title = {Too Large; Data Reduction for Vision-Language Pre-Training}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2023}, pages = {3147-3157} }