论文标题
对抗半监督多域跟踪
Adversarial Semi-Supervised Multi-Domain Tracking
论文作者
论文摘要
多域学习的神经网络通过共享和共享参数来赋予来自不同领域的信息的有效组合。在视觉跟踪中，多域跟踪器的共享层中的新兴功能（接受各种序列训练）对于在看不见的视频中跟踪至关重要。但是，在完全共享的体系结构中，一些新兴功能仅在特定领域中有用，从而减少了学习特征表示的概括。我们提出了一种半监督的学习计划，使用对抗性学习，以鼓励他们之间的相互排斥，并利用未标记的储层来增强共同的特征，以鼓励他们之间的相互排斥，以分离域名和领域特定的特征。通过为每个序列采用这些功能和培训专用图层，我们构建了一个在不同类型的视频上表现出色的跟踪器。
Neural networks for multi-domain learning empowers an effective combination of information from different domains by sharing and co-learning the parameters. In visual tracking, the emerging features in shared layers of a multi-domain tracker, trained on various sequences, are crucial for tracking in unseen videos. Yet, in a fully shared architecture, some of the emerging features are useful only in a specific domain, reducing the generalization of the learned feature representation. We propose a semi-supervised learning scheme to separate domain-invariant and domain-specific features using adversarial learning, to encourage mutual exclusion between them, and to leverage self-supervised learning for enhancing the shared features using the unlabeled reservoir. By employing these features and training dedicated layers for each sequence, we build a tracker that performs exceptionally on different types of videos.
