In this work, by re-examining the “matching” nature of Anomaly Detection (AD), we propose
a novel AD framework that simultaneously enjoys new records of AD accuracy and …

One of the challenges for neural networks in real-life applications is the overconfident errors
these models make when the data is not from the original training distribution. Addressing …

In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative
that DNNs provide inference robust to external perturbations-both intentional and …

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 …

Existing out-of-distribution (OOD) methods have shown great success on balanced datasets
but become ineffective in long-tailed recognition (LTR) scenarios where 1) OOD samples …

Abstract Out-of-Distribution (OOD) Generalization aims to learn robust models that
generalize well to various environments without fitting to distribution-specific features …

In anomaly segmentation for complex driving scenes, state-of-the-art approaches utilize
anomaly scoring functions to calculate anomaly scores. For these functions, accurately …

Cross-scene classification focuses on setting up an effective domain adaptation (DA) way to
transfer the learnable knowledge from source to target domain, which can be reasonably …

Deep learning has led to remarkable strides in scene understanding with panoptic
segmentation emerging as a key holistic scene interpretation task. However, the …

Abstract Detecting Out-of-Distribution (OOD) inputs is essential for reliable deep learning in
the open world. However, most existing OOD detection methods have been developed …