Abstract
In this report, we present the technical details of our submission to the EPIC-KITCHENS-100 Unsupervised Domain Adaptation (UDA) Challenge for Action Recognition 2022. This task aims to adapt an action recognition model trained on a labeled source domain to an unlabeled target domain. To achieve this goal, we propose an action-aware domain adaptation framework that leverages the prior knowledge induced from the action recognition task during the adaptation. Specifically, we disentangle the source features into action-relevant features and action-irrelevant features using the learned action classifier and then align the target features with the action-relevant features. To further improve the action prediction performance, we exploit the verb-noun co-occurrence matrix to constrain and refine the action predictions. Our final submission achieved the first place in terms of top-1 action recognition accuracy.
Abstract (translated)
在本报告中,我们介绍了我们对 EPIC-KITCHENS-100 无监督 domain 适应挑战(UDA)2022 的提交技术细节。该任务旨在将在一个标记源域中训练的行动识别模型适应到一个未标记的目标域。为了实现这一目标,我们提出了一种行动意识的 domain 适应框架,该框架利用在适应期间从行动识别任务中获得的前知来利用。具体来说,我们使用学习的行动分类器将源特征分离为行动相关和行动无关的特征,然后将目标特征与行动相关特征对齐。为了进一步提高行动预测性能,我们利用动词-名词共现矩阵来限制和优化行动预测。我们的最终提交在准确率排名前1位方面取得了第一名。
URL
https://arxiv.org/abs/2301.12436
