Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint information among classes. To this end, JointEnergy, which is a representative multi-label OOD inference criterion, summarizes the logits of all the classes. However, we find that JointEnergy can produce an imbalance problem in OOD detection, especially when the model lacks enough discrimination ability. Specifically, we find that the samples only related to minority classes tend to be classified as OOD samples due to the ambiguous energy decision boundary. Besides, imbalanced multi-label learning methods, originally designed for ID ones, would not be suitable for OOD detection scenarios, even producing a serious negative transfer effect. In this paper, we resort to auxiliary outlier exposure (OE) and propose an unknown-aware multi-label learning framework to reshape the uncertainty energy space layout. In this framework, the energy score is separately optimized for tail ID samples and unknown samples, and the energy distribution gap between them is expanded, such that the tail ID samples can have a significantly larger energy score than the OOD ones. What's more, a simple yet effective measure is designed to select more informative OE datasets. Finally, comprehensive experimental results on multiple multi-label and OOD datasets reveal the effectiveness of the proposed method.

翻译：多标签分布外（OOD）检测旨在从多标签分布内（ID）样本中识别OOD样本。与多类别场景相比，建模类别间的联合信息至关重要。为此，作为代表性多标签OOD推断准则的JointEnergy通过汇总所有类别的逻辑值来实现检测。然而，我们发现JointEnergy在OOD检测中可能引发不平衡问题，尤其在模型判别能力不足时更为显著。具体而言，由于能量决策边界模糊，仅与少数类别相关的样本易被误判为OOD样本。此外，原本为ID场景设计的不平衡多标签学习方法并不适用于OOD检测，甚至可能产生严重的负迁移效应。本文借助辅助离群暴露（OE）技术，提出一种未知感知的多标签学习框架以重构不确定性能量空间布局。该框架分别针对尾部ID样本和未知样本优化能量分数，并扩大两者间的能量分布间隙，使得尾部ID样本的能量分数显著高于OOD样本。此外，我们设计了一种简单而有效的度量方法来筛选信息量更高的OE数据集。最终，在多组多标签与OOD数据集上的综合实验结果验证了所提方法的有效性。