Out-of-distribution (OOD) detection identifies test samples that differ from
the training data, which is critical to ensuring the safety and reliability of
machine learning (ML) systems. While a plethora of methods have been developed
to detect uni-modal OOD samples, only a few have focused on multi-modal OOD
detection. Current contrastive learning-based methods primarily study
multi-modal OOD detection in a scenario where both a given image and its
corresponding textual description come from a new domain. However, real-world
deployments of ML systems may face more anomaly scenarios caused by multiple
factors like sensor faults, bad weather, and environmental changes. Hence, the
goal of this work is to simultaneously detect from multiple different OOD
scenarios in a fine-grained manner. To reach this goal, we propose a
general-purpose weakly-supervised OOD detection framework, called WOOD, that
combines a binary classifier and a contrastive learning component to reap the
benefits of both. In order to better distinguish the latent representations of
in-distribution (ID) and OOD samples, we adopt the Hinge loss to constrain
their similarity. Furthermore, we develop a new scoring metric to integrate the
prediction results from both the binary classifier and contrastive learning for
identifying OOD samples. We evaluate the proposed WOOD model on multiple
real-world datasets, and the experimental results demonstrate that the WOOD
model outperforms the state-of-the-art methods for multi-modal OOD detection.
Importantly, our approach is able to achieve high accuracy in OOD detection in
three different OOD scenarios simultaneously. The source code will be made
publicly available upon publication.

这篇论文提出了一种名为 WOOD 的通用弱监督 OOD 检测框架，同时检测多种不同的 OOD 场景，并结合二元分类器和对比学习组件以取得双方的优势，实验证明该模型在多模态 OOD 检测方面超过了现有方法。