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 …

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 …

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 …

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 …

Recently, large-scale pre-trained Vision-Language Models (VLMs) have demonstrated great
potential in learning open-world visual representations, and exhibit remarkable performance …

Shifting deep learning models from lab environments to real-world settings entails preparing
them to handle unforeseen conditions, including the chance of encountering novel objects …

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

Traditional object detection methods operate under the closed-set assumption, where
models can only detect a fixed number of objects predefined in the training set. Recent …