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。