Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2026

Abstract

Current weakly supervised video anomaly detection (WSVAD) task aims to achieve frame-level anomalous event detection with only coarse video-level annotations available. Existing works typically involve extracting global features from full-resolution video frames and training frame-level classifiers to detect anomalies in the temporal dimension. However, most anomalous events tend to occur in localized spatial regions rather than the entire video frames, which implies existing frame-level feature based works may be misled by the dominant background information and lack the interpretation of the detected anomalies. To address this dilemma, this paper introduces a novel method called STPrompt that learns spatio-temporal prompt embeddings for weakly supervised video anomaly detection and localization (WSVADL) based on pre-trained vision-language models (VLMs). Our proposed method employs a two-stream network structure, with one stream focusing on the temporal dimension and the other primarily on the spatial dimension. By leveraging the learned knowledge from pre-trained VLMs and incorporating natural motion priors from raw videos, our model learns prompt embeddings that are aligned with spatio-temporal regions of videos (e.g., patches of individual frames) for identify specific local regions of anomalies, enabling accurate video anomaly detection while mitigating the influence of background information. Without relying on detailed spatio-temporal annotations or auxiliary object detection/tracking, our method achieves state-of-the-art performance on three public benchmarks for the WSVADL task.

Keywords

Scene anomaly detection, Visual content-based indexing and retrieval, Video anomaly detection, Spatio-temporal detection, Language-image pre-training

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

Proceedings of 32nd ACM International Conference on Multimedia (ACM MM 2024) : Melbourne, Australia, October 28 - November 1

First Page

9301

Last Page

9310

Identifier

10.1145/3664647.3681442

Publisher

Association for Computing Machinery

City or Country

Melbourne, Australia

Additional URL

https://doi.org/10.1145/3664647.3681442

Download

Share

COinS