Open-set Unsupervised Video Domain Adaptation (OUVDA) deals with the task of adapting an action recognition model from a labelled source domain to an unlabelled target domain that contains "target-private" categories, which are present in the target but absent in the source. In this work we deviate from the prior work of training a specialized open-set classifier or weighted adversarial learning by proposing to use pre-trained Language and Vision Models (CLIP). The CLIP is well suited for OUVDA due to its rich representation and the zero-shot recognition capabilities. However, rejecting target-private instances with the CLIP's zero-shot protocol requires oracle knowledge about the target-private label names. To circumvent the impossibility of the knowledge of label names, we propose AutoLabel that automatically discovers and generates object-centric compositional candidate target-private class names. Despite its simplicity, we show that CLIP when equipped with AutoLabel can satisfactorily reject the target-private instances, thereby facilitating better alignment between the shared classes of the two domains. The code is available.

翻译：开放集无监督视频域适应（OUVDA）任务旨在将动作识别模型从带标签的源域适应到包含“目标私有”类别的无标签目标域，这些类别存在于目标域却在源域中缺失。本研究区别于以往训练专用开放集分类器或加权对抗学习的做法，提出利用预训练的语言与视觉模型（CLIP）。CLIP凭借其丰富的表征能力和零样本识别能力，天然适配OUVDA任务。然而，使用CLIP的零样本协议拒绝目标私有实例需要关于目标私有标签名称的先验知识。为规避这种标签名称不可知的问题，我们提出AutoLabel框架，其能自动发现并生成以对象为中心的复合候选目标私有类别名称。尽管方法简洁，但实验表明，配备AutoLabel的CLIP可有效拒绝目标私有实例，从而促进两域共享类别的更好对齐。相关代码已公开。