Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification
<|reference_start|>Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification: Vision-language foundation models have been incredibly successful in a wide range of downstream computer vision tasks using adaptation methods. However, due to the high cost of obtaining pre-training datasets, pairs with weak image-text correlation in the data exist in large numbers. We call them weak-paired samples. Due to the limitations of these weak-paired samples, the pre-training model are unable to mine all the knowledge from pre-training data. The existing adaptation methods do not consider the missing knowledge, which may lead to crucial task-related knowledge for the downstream tasks being ignored. To address this issue, we propose a new adaptation framework called Data Adaptive Traceback (DAT). Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data to enable the downstream tasks. Furthermore, we adopt a pseudo-label-based semi-supervised technique to reuse the pre-training images and a vision-language contrastive learning method to address the confirmation bias issue in semi-supervised learning. We conduct extensive experiments that show our proposed DAT approach meaningfully improves various benchmark datasets performance over traditional adaptation methods by simply.<|reference_end|>
@article{peng2024data,
    title={Data Adaptive Traceback for Vision-Language Foundation Models in Image
  Classification},
    author={Wenshuo Peng, Kaipeng Zhang, Yue Yang, Hao Zhang, Yu Qiao},
    journal={arXiv preprint arXiv:2407.08787},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08787},
    primaryClass={cs.CV}
}