A Hierarchical Hard Negative Sampling Strategy for Robust Out-of-Distribution Object Detection

Out-of-distribution (OOD) detection is crucial for deploying models in open-world environments. This process aims to mitigate the issue of overconfident predictions, which is a common problem for models designed for closed-domain tasks when they encounter OOD data. Recent progress in OOD detection has shown that integrating auxiliary datasets during model training can greatly enhance OOD detection performance. However, existing methods tend to heavily depend on these auxiliary datasets to establish the decision boundary for in-distribution (ID) data, while not adequately addressing OOD object detection in safety–critical applications. In this article, we propose object-level OOD detection and introduce a hierarchical hard negative sampling (HNS) strategy that does not necessitate auxiliary data. Specifically, we offer a new metric that strategically considers difficult negatives near the decision boundary between inter-class and intra-class instances. Inspired by adversarial thinking, we sample outliers for each class, ensuring that the negative samples capture both diversity and informative traits. We conducted comprehensive experiments on three public datasets. The results demonstrate that HNS performs superiorly in object-level OOD detection, even without auxiliary datasets. The source code can be accessed at: https://github.com/Aurevior1/HNS .