arXiv:2606. 07669v1 Announce Type: cross Abstract: Deploying Video Anomaly Detection (VAD) in real-world surveillance faces a fundamental tension between the demand for high-level semantics to ensure effectiveness and the limited computational resources of edge devices.
arXiv:2604. 26508v2 Announce Type: replace-cross Abstract: Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms.
arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.
arXiv:2608. 08219v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is a critical yet challenging task due to the complex and diverse nature of real-world scenarios.
arXiv:2606. 14724v1 Announce Type: cross Abstract: Video anomaly detection in surveillance settings must balance detection accuracy against real-time throughput, a tension that existing methods address either through stronger feature extractors or more efficient architectures, but rarely both.
arXiv:2608. 06876v1 Announce Type: cross Abstract: In the era of Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS), Federated Learning (FL) offers a promising decentralized intelligence paradigm for Video Anomaly Recognition (VAR).
arXiv:2607. 09114v1 Announce Type: cross Abstract: Video anomaly detection (VAD) is critical for automated surveillance but remains fragile under challenging conditions such as illumination variations, fast motion, and complex backgrounds when relying solely on visible light videos.
arXiv:2604. 23724v3 Announce Type: replace-cross Abstract: Expressway video anomaly detection is essential for safety management.
arXiv:2606. 17798v1 Announce Type: cross Abstract: Despite the remarkable progress of Video Large Language Models (Video-LLMs), current online architectures still struggle to simultaneously process continuous video streams, decide autonomously when to respond, and preserve long-horizon contextual memory.
arXiv:2604. 06435v2 Announce Type: replace-cross Abstract: Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare.
arXiv:2607. 18142v1 Announce Type: cross Abstract: Industrial Video Anomaly Detection (IVAD) aims to identify anomalous objects and events in an industrial process, which is crucial for modern manufacturing and quality control systems.
arXiv:2603. 26842v3 Announce Type: replace-cross Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.
arXiv:2506. 03162v3 Announce Type: replace-cross Abstract: The rapid proliferation of surveillance cameras has increased the demand for automated violence detection.