Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification

• URL: http://arxiv.org/abs/2407.08787v1
• Date: Thu, 11 Jul 2024 18:01:58 GMT
• Title: Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification
• Authors: Wenshuo Peng, Kaipeng Zhang, Yue Yang, Hao Zhang, Yu Qiao,
• Abstract summary: We propose a new adaptation framework called Data Adaptive Traceback. Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data. 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.
• Score: 34.37262622415682
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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