In real-world recognition/classification tasks, limited by various objective factors, it is usually
difficult to collect training samples to exhaust all classes when training a recognizer or …

The world we see is ever-changing and it always changes with people, things, and the
environment. Domain is referred to as the state of the world at a certain moment. A research …

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 recognition (OSR), aiming to simultaneously classify the seen classes and identify
the unseen classes as' unknown', is essential for reliable machine learning. The key …

Traditional classifiers are deployed under closed-set setting, with both training and test
classes belong to the same set. However, real-world applications probably face the input of …

Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Open-set classification is a problem of handling'unknown'classes that are not contained in
the training dataset, whereas traditional classifiers assume that only known classes appear …

Abstract Models trained for classification often assume that all testing classes are known
while training. As a result, when presented with an unknown class during testing, such …

Deep neural networks have achieved state-of-the-art performance in a wide range of
recognition/classification tasks. However, when applying deep learning to real-world …

The application of deep learning in robotics leads to very specific problems and research
questions that are typically not addressed by the computer vision and machine learning …