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Yuchuan Li

2 accepted papers

2026

UNI-OOD: Unified Object- and Image-level Out-of-Distribution Detection via Cross-Context Attentive Vision-Language Modeling

CVPR 2026

Out-of-distribution (OOD) detection is a key requirement for reliable deployment in open-world environments, where a model must recognize inputs that fall outside the semantic scope of known concepts. While recent advances in vision-language models (VLMs) have achieved strong results in image-level

Cited by 0SourceScholar
2025

Beyond Clean Training Data: A Versatile and Model-Agnostic Framework for Out-of-Distribution Detection with Contaminated Training Data

CVPR 2025poster

In real-world AI applications, training datasets are often contaminated, containing a mix of in-distribution (ID) and out-of-distribution (OOD) samples without labels. This contamination poses a significant challenge for developing and training OOD detection models, as nearly all existing methods as…

Cited by 0SourcePDFScholar