Most cross-domain unsupervised Video Anomaly Detection (VAD) works assume that at
least few task-relevant target domain training data are available for adaptation from the …

With the popularity of video surveillance technology, people are paying more and more
attention to how to detect abnormal states or events in videos in time. Therefore, real-time …

Multi-task learning faces two challenging issues:(1) the high cost of annotating labels for all
tasks and (2) balancing the training progress of various tasks with different natures. To …

Abstract All-in-one (AiO) frameworks restore various adverse weather degradations with a
single set of networks jointly. To handle various weather conditions an AiO framework is …

This paper proposes an approach for anomalous sound detection that incorporates outlier
exposure and inlier modeling within a unified framework by multitask learning. While outlier …

Federated Learning (FL) is an emerging paradigm for training machine learning models
across decentralized edge devices, ensuring data privacy and reducing computational tasks …

The broad learning system (BLS) featuring lightweight, incremental extension, and strong
generalization capabilities has been successful in its applications. Despite these …

In many human-computer interaction applications, fast and accurate hand tracking is
necessary for an immersive experience. However, raw hand motion data can be flawed due …

For intelligent surveillance systems, abnormal event detection (AED) automatically analyses
monitoring video sequences and detects abnormal objects or strange human actions at the …

Anomaly detection for video surveillance gains more attention as the number of deployed
cameras constantly increases while the state-of-the-art (SOTA) machine learning methods …