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.

本篇论文提出了一个新的基于能量的混合模型用于 few-shot 开放集识别任务，利用类内和像素级特征来学习全局能量评分，强制模型对偏离 few-shot 示例的样本分配大的能量分数，在三个标准数据集上取得了最优秀的性能。

基于全球本地化能量的少样本开放式识别学习

Glocal Energy-based Learning for Few-Shot Open-Set Recognition

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).

该论文提出了一种新颖的方法来解决 few-shot open-set recognition 问题；首先，利用 background features 提取 pseudo unseen classes，将决策边界保留给 unseen classes，以更有效地学习分类器；通过对 tieredImageNet、miniImageNet 和 CUB 三个基准数据集的实验，实现了超越多个基准的 state-of-the-art 结果。

利用背景作为未知元素的小样本开放式识别

Few-shot Open-set Recognition Using Background as Unknowns

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.

该研究提出了一种基于负类样本生成和使用动态拒绝边界的少样本开放集识别方法，有效地处理开放集分类中的数据限制问题。