Video Anomaly Detection (VAD) has emerged as a pivotal task in computer vision, with broad relevance across multiple fields. Recent advances in deep learning have driven significant progress in this area, yet the field remains fragmented across domains and learning paradigms. This survey offers a comprehensive perspective on VAD, systematically organizing the literature across various supervision levels, as well as adaptive learning methods such as online, active, and continual learning. We examine the state of VAD across three major application categories: human-centric, vehicle-centric, and environment-centric scenarios, each with distinct challenges and design considerations. In doing so, we identify fundamental contributions and limitations of current methodologies. By consolidating insights from subfields, we aim to provide the community with a structured foundation for advancing both theoretical understanding and real-world applicability of VAD systems. This survey aims to support researchers by providing a useful reference, while also drawing attention to the broader set of open challenges in anomaly detection, including both fundamental research questions and practical obstacles to real-world deployment.

The success of generative modeling in continuous domains has led to a surge of interest in generating discrete data such as molecules, source code, and graphs.

Finally, we illustrate how our framework can be used for exploratory data analysis.

Decision Diagram (AOMDD) which represents all its optimal solutions.

Neural Information Processing Systems http://nips.cc/

However, models are rarely well-specified in practice.

We use this metric to evaluate the performance versus resource trade-off relevant to the application of interest, and we propose a strategy that selects the device settings that can optimally meet users' requirements.

We would like to thank the reviewers for their thoughtful comments and valuable suggestions. We will clarify this point in the paper. Our algorithms are agnostic to the leaf distributions used. Thanks for this valuable feedback, we will improve the pseudocode as you suggest. As such, there is memory overhead but no computational overhead.