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 …

Utilizing auxiliary outlier datasets to regularize the machine learning model has
demonstrated promise for out-of-distribution (OOD) detection and safe prediction. Due to the …

Machine learning has achieved remarkable success in many applications. However,
existing studies are largely based on the closed-world assumption, which assumes that the …

Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world
intelligent systems. Despite the emergence of an increasing number of OOD detection …

Given an untrimmed video, temporal sentence grounding (TSG) aims to locate a target
moment semantically according to a sentence query. Although previous respectable works …

We present a novel vision-language prompt learning approach for few-shot out-of-
distribution (OOD) detection. Few-shot OOD detection aims to detect OOD images from …

Out-of-distribution (OOD) detection is the problem of identifying inputs which are unrelated to
the in-distribution task. The OOD detection performance when the in-distribution (ID) is …

Machine learning models deployed in the wild naturally encounter unlabeled samples from
both known and novel classes. Challenges arise in learning from both the labeled and …

Machine learning methods can detect Android malware with very high accuracy. However,
these classifiers have an Achilles heel, concept drift: they rapidly become out of date and …

Video domain generalization aims to learn generalizable video classification models for
unseen target domains by training in a source domain. A critical challenge of video domain …