We study the problem of few-shot open-set recognition (FSOR), which learns a recognition system capable of both fast adaptation to new classes with limited labeled examples and rejection of unknown negative samples. Traditional large-scale open-set methods have been shown ineffective for FSOR problem due to data limitation. Current FSOR methods typically calibrate few-shot closed-set classifiers to be sensitive to negative samples so that they can be rejected via thresholding. However, threshold tuning is a challenging process as different FSOR tasks may require different rejection powers. In this paper, we instead propose task-adaptive negative class envision for FSOR to integrate threshold tuning into the learning process. Specifically, we augment the few-shot closed-set classifier with additional negative prototypes generated from few-shot examples. By incorporating few-shot class correlations in the negative generation process, we are able to learn dynamic rejection boundaries for FSOR tasks. Besides, we extend our method to generalized few-shot open-set recognition (GFSOR), which requires classification on both many-shot and few-shot classes as well as rejection of negative samples. Extensive experiments on public benchmarks validate our methods on both problems.
翻译:我们研究少见的开放定位识别(FSOR)问题,它学会了一种既能快速适应有有限标签的例子的新类别又能拒绝未知负面样本的承认系统。传统的大型开放定位方法由于数据限制,已证明对FSOR问题无效。目前的FSOR方法通常校准少见的封闭定位分类器,对负面样本敏感,以便通过阈值拒绝它们。然而,阈值调调调调是一个具有挑战性的过程,因为不同的FSOR任务可能要求不同的拒绝权。在本文中,我们建议为FSOR提出任务适应性负级设想,以便将阈值调整纳入学习进程。具体地说,我们用从微小例子中产生的更多负位原型来增加少见的封闭定位分类器。在负位生成过程中,我们通过将微粒级定级的关联性学习FSOR任务的动态拒绝边界。此外,我们将我们的方法推广到一般的、少见的开放定位识别(FOSOR),这需要对多发和几发的分类进行分类,并拒绝否定负面样本。关于公共基准方法的广泛实验。