论文标题
使用背景作为未知数的几个开放式识别
Few-shot Open-set Recognition Using Background as Unknowns
论文作者
论文摘要
很少有开放式识别旨在对可见类的培训数据进行有限的培训数据进行分类和新型图像。这项任务的挑战是，该模型不仅需要学习判别性分类器，以用很少的培训数据对预定的类进行分类，而且还要拒绝从未见过的培训时间中从未出现过的培训类别的输入。在本文中，我们建议从两个新方面解决问题。首先，我们没有像在标准的封闭式分类中那样学习看到的类之间的决策边界，而是为看不见的类保留空间，因此位于这些区域中的图像被认为是看不见的类。其次，为了有效地学习此类决策边界，我们建议利用所见类中的背景特征。由于这些背景区域没有显着促进近距离分类的决定，因此自然地将它们用作分类器学习的伪阶层。我们的广泛实验表明，我们提出的方法不仅要优于多个基准，而且还为三个流行的基准测试，即tieredimagenet，miniimagenet和Caltech-uscd birds-birds-birds-2011（Cub）设定了新的最新结果。
Few-shot open-set recognition aims to classify both seen and novel images given only limited training data of seen classes. The challenge of this task is that the model is required not only to learn a discriminative classifier to classify the pre-defined classes with few training data but also to reject inputs from unseen classes that never appear at training time. In this paper, we propose to solve the problem from two novel aspects. First, instead of learning the decision boundaries between seen classes, as is done in standard close-set classification, we reserve space for unseen classes, such that images located in these areas are recognized as the unseen classes. Second, to effectively learn such decision boundaries, we propose to utilize the background features from seen classes. As these background regions do not significantly contribute to the decision of close-set classification, it is natural to use them as the pseudo unseen classes for classifier learning. Our extensive experiments show that our proposed method not only outperforms multiple baselines but also sets new state-of-the-art results on three popular benchmarks, namely tieredImageNet, miniImageNet, and Caltech-USCD Birds-200-2011 (CUB).
