[2603.25250] Activation Matters: Test-time Activated Negative Labels for OOD Detection with Vision-Language Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.25250 (cs) [Submitted on 26 Mar 2026] Title:Activation Matters: Test-time Activated Negative Labels for OOD Detection with Vision-Language Models Authors:Yabin Zhang, Maya Varma, Yunhe Gao, Jean-Benoit Delbrouck, Jiaming Liu, Chong Wang, Curtis Langlotz View a PDF of the paper titled Activation Matters: Test-time Activated Negative Labels for OOD Detection with Vision-Language Models, by Yabin Zhang and 6 other authors View PDF HTML (experimental) Abstract:Out-of-distribution (OOD) detection aims to identify samples that deviate from in-distribution (ID). One popular pipeline addresses this by introducing negative labels distant from ID classes and detecting OOD based on their distance to these labels. However, such labels may present poor activation on OOD samples, failing to capture the OOD characteristics. To address this, we propose \underline{T}est-time \underline{A}ctivated \underline{N}egative \underline{L}abels (TANL) by dynamically evaluating activation levels across the corpus dataset and mining candidate labels with high activation responses during the testing process. Specifically, TANL identifies high-confidence test images online and accumulates their assignment probabilities over the corpus to construct a label activation metric. Such a metric leverages historical test samples to adaptively align with the test distribution, enabling the selection of distribution-adaptive activated negative...