论文标题
要在看不见的领域中识别看不见的类别
Towards Recognizing Unseen Categories in Unseen Domains
论文作者
论文摘要
当前深层视觉识别系统在训练过程中遇到了班级和场景的新图像时，出现了严重的性能下降。因此，零拍学习（ZSL）的核心挑战是应对语义换档，而域适应性和域概括（DG）的主要挑战是域转移。虽然历史上ZSL和DG任务是孤立地解决的，但这项工作的发展却以雄心勃勃的目标共同解决，即。通过识别看不见的域中看不见的视觉概念。我们介绍了Cumix（用于识别看不见域中看不见的类别的课程），这是一种可以解决ZSL，DG和ZSL+DG的整体算法。 Cumix的关键思想是使用来自看不见的域和类别的图像和功能来模拟测试时间域和语义转移，该图像和功能通过混合训练过程中可用的多个源域和类别而生成的类别。此外，制定了基于课程的混合政策，以生成日益复杂的培训样本。使用域基准测试的标准SL和DG数据集以及ZSL+DG上的结果证明了我们方法的有效性。
Current deep visual recognition systems suffer from severe performance degradation when they encounter new images from classes and scenarios unseen during training. Hence, the core challenge of Zero-Shot Learning (ZSL) is to cope with the semantic-shift whereas the main challenge of Domain Adaptation and Domain Generalization (DG) is the domain-shift. While historically ZSL and DG tasks are tackled in isolation, this work develops with the ambitious goal of solving them jointly,i.e. by recognizing unseen visual concepts in unseen domains. We presentCuMix (CurriculumMixup for recognizing unseen categories in unseen domains), a holistic algorithm to tackle ZSL, DG and ZSL+DG. The key idea of CuMix is to simulate the test-time domain and semantic shift using images and features from unseen domains and categories generated by mixing up the multiple source domains and categories available during training. Moreover, a curriculum-based mixing policy is devised to generate increasingly complex training samples. Results on standard SL and DG datasets and on ZSL+DG using the DomainNet benchmark demonstrate the effectiveness of our approach.
