DynProto dynamically captures coarse OOD patterns confused with each ID class and refines them into prototypes during testing to enable similarity-based OOD detection that outperforms prior methods on ImageNet benchmarks.
How to exploit hyperspherical embeddings for out-of-distribution detection? InICLR
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
TTL dynamically learns OOD textual semantics from unlabeled test streams via prompt updates, purification, and a knowledge bank to improve detection performance in pretrained VLMs.
citing papers explorer
-
TTL: Test-time Textual Learning for OOD Detection with Pretrained Vision-Language Models
TTL dynamically learns OOD textual semantics from unlabeled test streams via prompt updates, purification, and a knowledge bank to improve detection performance in pretrained VLMs.