通过多任务训练提高视觉变形能力

Woojin Ahn, G. Yang, H. Choi, M. Lim, Tae-Koo Kang
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引用次数: 0

摘要

自监督学习方法通过从大量未标记数据中学习视觉表示,在改善现有网络性能方面表现出色。本文提出了一种视觉变压器端到端的多任务自监督方法。该网络有两个任务:绘制、位置预测。给定一个被遮挡的图像,该网络预测缺失的像素信息,并预测给定拼图补丁的位置。通过分类实验,我们证明了与直接监督学习方法相比,该方法提高了网络的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Vision Transformer with Multi-Task Training
Self-supervised learning methods have shown excellent performance in improving the performance of existing networks by learning visual representations from large amounts of unlabeled data. In this paper, we propose a end-to-end multi-task self-supervision method for vision transformer. The network is given two task: inpainting, position prediction. Given a masked image, the network predicts the missing pixel information and also predicts the position of the given puzzle patches. Through classification experiment, we demonstrate that the proposed method improves performance of the network compared to the direct supervised learning method.
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