做 OOD 检测或 VLM 应用的开发者，这篇解决了负样本挖掘的假负问题，理论扎实且效果显著，值得直接参考代码复现。
本文提出一种去偏负样本挖掘方法，用于改进基于预训练视觉语言模型（VLM）的分布外（OOD）检测。现有方法依赖启发式规则从无标签语料中挖掘负标签，但存在严重的假负样本问题。作者通过理论框架校正负标签的采样偏差，将其转化为基于 ID 标签和无标签语料的蒙特卡洛采样。实验表明，该方法在多种 OOD 检测设置下达到新的最优性能。代码已开源。
Debiased Negative Mining Improves Out-of-distribution Detection with Pre-trained Vision-Language Models
Aiming at identifying unexpected inputs from unknown classes, out-of-distribution (OOD) detection has emerged as a pivotal approach to enhancing the reliability of machine learning models. This paper focuses on the burgeoning paradigm of post-hoc OOD detection with pre-trained vision-language models (VLMs), where a popular pipeline is to detect OOD inputs by examining their affinities between ID labels and negative labels, i.e., those semantically different from ID labels. Due to the unavailability of target OOD labels, existing works predominantly rely on heuristic rules to mine negative labels from unlabeled wild corpus data. Despite the empirical success, we argue that the power of VLM-based OOD detection has yet to be fully unleashed since the notorious false negative problem is far from addressed in the literature. With this motivation, we are interested in addressing the challenge of mining true negative labels for OOD scoring. To this end, we develop a theoretical framework for correcting the sampling bias of negatives labels by indirectly approximating the distribution of negative labels. Perhaps surprisingly, we show that the debiased negative mining can be naturally converted into Monte-Carlo sampling based on ID labels and the unlabeled wild corpus data. Extensive experiments empirically manifest that our method establishes a new state-of-the-art in a variety of OOD detection setups. Code is publicly available at \href{https://github.com/60pen9/Debiased-Negative-Mining-Improves-OOD-Detection-with-Pre-trained-VLMs}{\textcolor{red}{here}}.