Unsupervised domain adaptation which aims to adapt models trained on a
labeled source domain to a completely unlabeled target domain has attracted
much attention in recent years. While many domain adaptation techniques have
been proposed for images, the problem of unsupervised domain adaptation in
videos remains largely underexplored. In this paper, we introduce Contrast and
Mix (CoMix), a new contrastive learning framework that aims to learn
discriminative invariant feature representations for unsupervised video domain
adaptation. First, unlike existing methods that rely on adversarial learning
for feature alignment, we utilize temporal contrastive learning to bridge the
domain gap by maximizing the similarity between encoded representations of an
unlabeled video at two different speeds as well as minimizing the similarity
between different videos played at different speeds. Second, we propose a novel
extension to the temporal contrastive loss by using background mixing that
allows additional positives per anchor, thus adapting contrastive learning to
leverage action semantics shared across both domains. Moreover, we also
integrate a supervised contrastive learning objective using target
pseudo-labels to enhance discriminability of the latent space for video domain
adaptation. Extensive experiments on several benchmark datasets demonstrate the
superiority of our proposed approach over state-of-the-art methods. Project
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本文介绍了 Contrast and Mix（CoMix）的新对比学习框架，旨在为无监督的视频领域适应学习具有判别不变性的特征表示。与现有方法依靠对抗学习进行特征对齐不同，我们利用时间对比学习通过最大化未标记视频在两个不同速度下的编码表示的相似性以及最小化不同速度下播放的不同视频的相似性来弥合领域差距，还利用背景混合提出了一种用于时间对比性损失的新型扩展，从而允许每个锚点附加附加正物，增加视频领域适应的语义分享。此外，我们还使用目标伪标签集成了有监督的对比性学习目标，以增强视频领域适应的潜在空间的可区分性。在几个基准数据集上进行的广泛实验验证了我们所提出的方法的优越性。