TINS: Test-time ID-prototype-separated Negative Semantics Learning for OOD Detection

arXiv:2605.10756v1 Announce Type: new Abstract: Vision-language models enable OOD detection by comparing image alignment with ID labels and negative semantics. Existing negative-label-based methods mainly rely on static negative labels constructed before inference, limiting their ability to cover diverse and evolving OOD concepts. Although test-time expansion provides a natural solution, naively learning negative semantics from potential OOD samples may introduce hard ID contamination. To address this issue, we propose a extbf{T}est-time extbf{I}D-prototype-separated extbf{N}egative extbf{S}emantics learning method, termed extbf{TINS}. TINS learns sample-specific negative text embeddings via image-to-text modality inversion and introduces ID-prototype-separated regularization to keep them separated from ID semantics. To further stabilize negative semantics expansion, TINS employs group-wise aggregation scoring and a buffer update strategy. Extensive experiments across Four-OOD, OpenOOD, Temporal-shift, and Various ID settings show consistent improvements over strong baselines. Notably, on the Four-OOD benchmark with ImageNet-1K as ID, TINS reduces the average FPR95 from 14.04% to 6.72%. Our code is available at https://github.com/zxk1212/tins.

Source: arXiv cs.CV | 2026-05-12