AI RESEARCH
TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models
arXiv CS.CL
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ArXi:2604.15756v1 Announce Type: new Vision-language models (VLMs) such as CLIP exhibit strong Out-of-distribution (OOD) detection capabilities by aligning visual and textual representations. Recent CLIP-based test-time adaptation methods further improve detection performance by incorporating external OOD labels. However, such labels are finite and fixed, while the real OOD semantic space is inherently open-ended. Consequently, fixed labels fail to represent the diverse and evolving OOD semantics encountered in test streams. To address this limitation, we