Abstract Out-of-distribution (OOD) detection is vital to safety-critical machine learning
applications and has thus been extensively studied, with a plethora of methods developed in …

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 …

Open-set action recognition (OSAR) aims to learn a recognition framework capable of both
classifying known classes and identifying unknown actions in open-set scenarios. Existing …

Neural networks suffer from catastrophic forgetting when sequentially learning tasks phase-
by-phase, making them inapplicable in dynamically updated systems. Class-incremental …

Deep learning models tend to forget their earlier knowledge while incrementally learning
new tasks. This behavior emerges because the parameter updates optimized for the new …

Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-
rich source domain to a label-scarce target domain without any constraints on the label …

Abstract Out-of-distribution (OOD) detection aims to identify OOD data based on
representations extracted from well-trained deep models. However, existing methods largely …

Given a pre-trained in-distribution (ID) model, the inference-time out-of-distribution (OOD)
detection aims to recognize OOD data during the inference stage. However, some …

Out-of-distribution (OOD) detection aims to detect testing samples far away from the in-
distribution (ID) training data, which is crucial for the safe deployment of machine learning …

We study the problem of semantic segmentation calibration. Lots of solutions have been
proposed to approach model miscalibration of confidence in image classification. However …