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TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning — arXiv:2608.11820

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.11820
License (as archived): CC0-1.0
Archived: 2026-10-19T05:49:32.009Z
SHA-256 of the archived text: 6d0220c3db31a5482fa2f91d9710e4e0746f0494257178aea409f373e7f8b9df
Stable URL of this page: https://metraipolis.com/documento/dc_0f98f20719fd

How to cite

TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning — arXiv:2608.11820 — arXiv (en), https://arxiv.org/abs/2608.11820 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0f98f20719fd (Metraipolis archive dc_0f98f20719fd, sha256 6d0220c3db31a5482fa2f91d9710e4e0746f0494257178aea409f373e7f8b9df)

BibTeX

@misc{dc0f98f20719fd,
  title = {TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning — arXiv:2608.11820},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.11820},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0f98f20719fd; sha256 6d0220c3db31a5482fa2f91d9710e4e0746f0494257178aea409f373e7f8b9df},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.11820. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Visual data is typically a prerequisite for training existing video anomaly detection (VAD) methods. However, obtaining sufficient annotated anomaly data for training is challenging and not scalable due to the rarity of anomaly data and the wide variety of abnormal events. In this work, we advocate that the effectiveness of treating texts as video sequences for the VAD model and propose a novel Text-Driven Video Anomaly Detection (TD-VAD) approach to break visual dependence. In contrast to the anomaly video data, text descriptions of abnormal events are easy to collect, and their class labels can be directly derived. Specifically, our method utilizes video-like text descriptions with temporal characteristics generated by LLM to train a VAD model, without any reliance on target-domain anomaly data. To capture the long- and short-range temporal logic of events, we design the event evolution causal attention module to model contextual dependencies across time. During inference, considering the domain gap between the texts and video sequences, we use the frozen CLIP encoder to extract embeddings of video frames to align the text modality while retaining crucial visual information. Comprehensive experiments on two large-scale VAD datasets, XD-Violence and UCF-Crime, demonstrate that our method outperforms prior one-class and unsupervised VAD methods by a large margin.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-13 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.11820 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:42:25.890Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).