OptLabel: Label Optimization for Negative-Label-Based OOD Detection

Negative-label-based methods have emerged as a strong paradigm for CLIP-based out-of-distribution (OOD) detection. However, existing methods are largely text-driven, defining positive labels directly from in-distribution (ID) class names and selecting negative labels based on textual dissimilarity from ID classes. Consequently, their positive and negative labels may poorly represent the ID distribution, and OOD samples close to the ID boundary remain underrepresented. To address these limitations, we propose OptLabel that optimizes the representation of positive and negative labels under the ID-supervised setting. OptLabel treats the positive and negative labels from existing zero-shot negative-label-based methods as a semantic prior and refines their representation using the available ID data and the near-boundary OOD samples synthesized by applying Gaussian perturbation to ID samples in the CLIP embedding space. We show that, in the high-dimensional CLIP space, the placement of these synthesized OOD samples has an intuitive angular interpretation, and is thus easily controllable. OptLabel is a lightweight solution by keeping the CLIP frozen and requiring no image generation, external models, or auxiliary datasets. On the ImageNet-1K benchmark, OptLabel outperforms SynOOD, the strongest ID-supervised baseline, by 2.41 AUROC and 6.51 FPR95 points. The source code is available in the Supplementary Material.