测试时分离ID原型，动态学习负向语义，提升未知类别检测精度
TINS: Test-time ID-prototype-separated Negative Semantics Learning for OOD Detection
- 通过图像转文本逆映射生成样本专属负向文本嵌入
- 在ImageNet-1K上将FPR95从14.04%降至6.72%
- 适合需要高可靠未知样本检测的视觉应用
视觉语言模型通过对比图像与已知标签和负向语义实现未知类别检测。现有基于负标签的方法主要依赖推理前构建的静态负标签，难以覆盖多样且动态演化的未知概念。尽管测试时扩展提供自然解决方案，但直接从潜在未知样本中学习负向语义可能导致硬性已知类别污染。为此，本文提出测试时ID原型分离的负向语义学习方法TINS。TINS通过图像到文本模态逆映射学习样本专属负向文本嵌入，并引入ID原型分离正则化以保持其与已知语义分离。为进一步稳定负向语义扩展，TINS采用分组聚合评分与缓冲区更新策略。在Four-OOD、OpenOOD、Temporal-shift及多种已知设置下广泛实验表明，性能持续优于强基线。尤其在ImageNet-1K作为已知类的Four-OOD基准上，平均FPR95由14.04%降至6.72%。代码已开源。
原文摘要 · Abstract (English)
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 \textbf{T}est-time \textbf{I}D-prototype-separated \textbf{N}egative \textbf{S}emantics learning method, termed \textbf{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.
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