Aug 2026· International Journal of Multimedia Information Retrieval· Vol 15· 0 citations· 99 references
Computer Science
TL;DR
This work proposes a novel taxonomy categorizing 34 existing language-driven VAD methods based on learning paradigm, core architecture, adaptation strategy, and functional output, and serves as a foundational resource for the VAD community, advancing the understanding and application of language-driven models in addressing complex VAD tasks.
This work proposes a novel Text-Driven Video Anomaly Detection (TD-VAD) approach, which utilizes video-like text descriptions with temporal characteristics generated by LLM to train a VAD model, without any reliance on target-domain anomaly data.
Shuang-Qing Zhang, Lei-Lei Ma, Zhao Wang et al.· 2 citations
In this paper, we propose MuST-VAD, a mutual structured learning framework for weakly supervised video anomaly detection (VAD) in which an anomaly detector and a large vision-language model (LVLM) exchange their acquired knowledge. Detectors in weakly supervised VAD learn anomaly scores from features extracted by a fix...
Satoshi Hashimoto, Hitoshi Nishimura, Mori Kurokawa· 0 citations
Experimental results show that the evaluation quantifies both benchmark anomaly detection and direct known-versus-unknown separation under the declared protocols, and that object prompts dominate the fused decision, while the auxiliary prompt groups do not yet contribute positively under the held-out-category protocol.
You-Xi Li, Xiang Chen, Li-Min Wang et al.· Scientific Reports· 0 citations
An in-depth survey of fifteen state-of-art methodologies including classical CNN models, temporal-spatial video recognition, transformer-based networks, explainable AI (XAI) models, and models that combine multimodal large language model (LLM) products are provided.
Shavnam Shavnam, Neha Dhiman· International Journal of Inn...· 0 citations
Video anomaly detection (VAD) is critical for automation systems and security surveillance. Recently, multimodal vision–language models (MLLMs) have attracted increasing attention due to their rich pre-trained knowledge and strong explainability. However, existing MLLM-based approaches struggle to adapt to real-world s...
Jiangyun Chen, Yuanjie Dang, Peng Chen et al.· IEEE Transactions on Informa...· 0 citations
Extensive experiments show SF-VAD achieves state-of-the-art detection results while offering a favorable trade-off between performance and annotation cost, and Frame-guided Progressive Learning (FPL), to generalize sparse frame supervision to event-level anomaly understanding.
Junxi Chen, Liang Li, Yun-Bin Tu et al.· Neural Information Processin...· 4 citations
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