Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closed-set classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of open-set samples, our model leverages both class-wise and pixel-wise features to learn a glocal energy-based score, in which a global energy score is learned using the class-wise features, while a local energy score is learned using the pixel-wise features. The model is enforced to assign large energy scores to samples that are deviated from the few-shot examples in either the class-wise features or the pixel-wise features, and to assign small energy scores otherwise. Experiments on three standard FSOR datasets show the superior performance of our model.

翻译：少样本开放集识别（FSOR）是一项极具实用价值的挑战性任务。其目标是将样本归类至由少量示例定义的预定义封闭集类别，同时能够拒识来自未知类别的样本。本研究针对FSOR任务提出了一种新型能量混合模型。该模型由两个分支组成：分类分支学习度量指标以将样本归类至封闭集类别，而能量分支则显式估计开放集概率。为实现开放集样本的全局检测，本模型同时利用类别级与像素级特征学习全局-局部能量评分，其中全局能量评分通过类别级特征学习，局部能量评分则通过像素级特征学习。模型被强制要求对在类别级或像素级特征上偏离少样本示例的样本赋予高能量分数，而对其他样本赋予低能量分数。在三个标准FSOR数据集上的实验结果表明，本模型具有卓越性能。