Supervised learning algorithms have shown limited use in the field of anomaly detection due
to the unpredictability and difficulty in acquiring abnormal samples. In recent years …

Animal behavior, as one of the most crucial indicators of animal health and welfare, provides
rich insights into animal physical and mental states. Automated animal activity recognition …

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 …

We present LSeg, a novel model for language-driven semantic image segmentation. LSeg
uses a text encoder to compute embeddings of descriptive input labels (eg," grass" or" …

Humans have a natural instinct to identify unknown object instances in their environments.
The intrinsic curiosity about these unknown instances aids in learning about them, when the …

Machine learning models often encounter samples that are diverged from the training
distribution. Failure to recognize an out-of-distribution (OOD) sample, and consequently …

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 …

Real-world machine learning systems need to analyze novel testing data that differs from the
training data. In K-way classification, this is crisply formulated as open-set recognition, core …

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 …

Abstract Universal Domain Adaptation (UNDA) aims to handle both domain-shift and
category-shift between two datasets, where the main challenge is to transfer knowledge …