Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …

Prompt learning has been widely adopted to efficiently adapt vision-language models
(VLMs), eg. CLIP, for few-shot image classification. Despite their success, most prompt …

Abstract Out-of-distribution (OOD) detection is crucial for model reliability, as it identifies
samples from unknown classes and reduces errors due to unexpected inputs. Vision …

Out-of-distribution (OOD) detection aims to detect test samples outside the training category
space, which is an essential component in building reliable machine learning systems …

Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the
reliability and trustworthiness of machine learning systems. Recently, Large Language …

Abstract Out-of-distribution (OOD) detection is critical for deploying machine learning models
in the open world. To design scoring functions that discern OOD data from the in-distribution …

Abstract Out-of-distribution (OOD) detection is crucial for deploying reliable machine
learning models in open-world applications. Recent advances in CLIP-based OOD detection …

Existing vision-language model (VLM)-based methods for out-of-distribution (OOD)
detection typically rely on similarity scores between input images and in-distribution (ID) text …

Large-scale pre-trained Vision-Language Models (VLMs) have exhibited impressive zero-
shot performance and transferability, allowing them to adapt to downstream tasks in a data …