In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition. Our approach, which we call UNITE, uses an image teacher model to adapt a video student model to the target domain. UNITE first employs self-supervised pre-training to promote discriminative feature learning on target domain videos using a teacher-guided masked distillation objective. We then perform self-training on masked target data, using the video student model and image teacher model together to generate improved pseudolabels for unlabeled target videos. Our self-training process successfully leverages the strengths of both models to achieve strong transfer performance across domains. We evaluate our approach on multiple video domain adaptation benchmarks and observe significant improvements upon previously reported results.

翻译：本文针对视频动作识别中的无监督领域自适应问题展开研究。我们提出的UNITE方法采用图像教师模型驱动视频学生模型向目标领域进行自适应。首先通过自监督预训练机制，利用教师引导的屏蔽蒸馏目标促进目标领域视频的判别性特征学习；继而基于屏蔽目标数据开展自训练，联合视频学生模型与图像教师模型为无标签目标视频生成更优的伪标签。该自训练过程有效融合两类模型的优势，实现了跨领域的强迁移性能。在多个视频领域自适应基准测试中，我们的方法较已有成果取得显著提升。