AutoLabel：基于CLIP的开放集视频域自适应框架
AutoLabel: CLIP-based framework for Open-set Video Domain Adaptation
- LTCI, Télécom Paris, Institut polytechnique de Paris（巴黎理工学院巴黎电信学院 LTCI）
- University of Trento（特伦托大学）
- Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出AutoLabel框架，利用CLIP的零样本能力自动生成目标私有类名，有效拒绝目标私有实例并促进跨域共享类对齐，解决开放集无监督视频域自适应问题。
AI中文摘要：
开放集无监督视频域自适应（OUVDA）处理将动作识别模型从有标签的源域适应到包含“目标私有”类别的无标签目标域的任务，这些类别存在于目标域但不存在于源域。在本工作中，我们偏离了训练专门的开放集分类器或加权对抗学习的先前工作，提出使用预训练的语言和视觉模型（CLIP）。由于其丰富的表示和零样本识别能力，CLIP非常适合OUVDA。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。为了规避标签名称知识的不可行性，我们提出了AutoLabel，它自动发现并生成以对象为中心的组合候选目标私有类名。尽管简单，我们展示了配备AutoLabel的CLIP可以令人满意地拒绝目标私有实例，从而促进两个域之间共享类的更好对齐。代码可用。
英文摘要：
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.
