Video understanding with large language models: A survey

Y Tang, J Bi, S Xu, L Song, S Liang, T Wang… - arxiv preprint arxiv …, 2023 - arxiv.org
With the burgeoning growth of online video platforms and the escalating volume of video
content, the demand for proficient video understanding tools has intensified markedly. Given …

Networking systems for video anomaly detection: A tutorial and survey

J Liu, Y Liu, J Lin, J Li, L Cao, P Sun, B Hu… - arxiv preprint arxiv …, 2024 - arxiv.org
The increasing utilization of surveillance cameras in smart cities, coupled with the surge of
online video applications, has heightened concerns regarding public security and privacy …

Advancing video anomaly detection: A concise review and a new dataset

L Zhu, L Wang, A Raj, T Gedeon, C Chen - arxiv preprint arxiv …, 2024 - arxiv.org
Video Anomaly Detection (VAD) finds widespread applications in security surveillance,
traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts …

Holmes-vad: Towards unbiased and explainable video anomaly detection via multi-modal llm

H Zhang, X Xu, X Wang, J Zuo, C Han, X Huang… - arxiv preprint arxiv …, 2024 - arxiv.org
Towards open-ended Video Anomaly Detection (VAD), existing methods often exhibit
biased detection when faced with challenging or unseen events and lack interpretability. To …

Large language models for anomaly and out-of-distribution detection: A survey

R Xu, K Ding - arxiv preprint arxiv:2409.01980, 2024 - arxiv.org
Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the
reliability and trustworthiness of machine learning systems. Recently, Large Language …

Deep learning for video anomaly detection: A review

P Wu, C Pan, Y Yan, G Pang, P Wang… - arxiv preprint arxiv …, 2024 - arxiv.org
Video anomaly detection (VAD) aims to discover behaviors or events deviating from the
normality in videos. As a long-standing task in the field of computer vision, VAD has …

Do language models understand time?

X Ding, L Wang - arxiv preprint arxiv:2412.13845, 2024 - arxiv.org
Large language models (LLMs) have revolutionized video-based computer vision
applications, including action recognition, anomaly detection, and video summarization …

Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight

X Ding, L Wang - arxiv preprint arxiv:2412.18298, 2024 - arxiv.org
Video anomaly detection (VAD) has witnessed significant advancements through the
integration of large language models (LLMs) and vision-language models (VLMs) …

Holmes-vau: Towards long-term video anomaly understanding at any granularity

H Zhang, X Xu, X Wang, J Zuo, X Huang, C Gao… - arxiv preprint arxiv …, 2024 - arxiv.org
How can we enable models to comprehend video anomalies occurring over varying
temporal scales and contexts? Traditional Video Anomaly Understanding (VAU) methods …

Vera: Explainable video anomaly detection via verbalized learning of vision-language models

M Ye, W Liu, P He - arxiv preprint arxiv:2412.01095, 2024 - arxiv.org
The rapid advancement of vision-language models (VLMs) has established a new paradigm
in video anomaly detection (VAD): leveraging VLMs to simultaneously detect anomalies and …