Learning Where and When: Patch-Based Spatiotemporal Localization in Weakly Supervised Video Anomaly Detection

• URL: http://arxiv.org/abs/2606.29498v1
• Date: Sun, 28 Jun 2026 16:52:29 GMT
• Title: Learning Where and When: Patch-Based Spatiotemporal Localization in Weakly Supervised Video Anomaly Detection
• Abstract summary: We introduce a patch-temporal framework for weakly supervised anomaly localization.<n>We propose a Proximitytemporal-Aware Topk selection strategy that enables the model to generate fine-grained spatial anomaly maps.<n>Our method surpasses existing state-the-art approaches across multiple benchmarks, yielding substantial gains in spatially supervised localization accuracy.
• Score: 12.15221664801473
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Weakly supervised video anomaly detection (WSVAD) has predominantly focused on temporal localization, identifying when anomalies occur while largely neglecting their spatial extent within frames. Yet, spatial localization is essential for interpretability and practical deployment in real-world settings. We introduce a patch-based spatiotemporal framework for weakly supervised anomaly localization that jointly models where and when anomalies occur. Our approach operates on grid-level patch features and learns region-level anomaly scores under a multiple instance learning paradigm. We further propose a Proximity-Aware Top-k spatiotemporal selection strategy that enables the model to generate fine-grained spatial anomaly maps without requiring bounding-box supervision during training. Our method surpasses existing state-of-the-art approaches across multiple benchmarks, yielding substantial gains in spatiotemporal localization accuracy. In addition, we release frame-level bounding-box annotations for the test sets of two widely used datasets, along with our code and pretrained models, providing new resources to facilitate future research in spatially grounded WSVAD.

Related papers

• Localizing to Debias: A Patch-Level Benchmark and Baseline for Weakly Supervised Spatial Anomaly Detection [7.474723197425266]
  We propose a sparse-temporal framework that bridges temporal anomaly detection with fine-grained spatial localization.<n>SST-WSDLVA focuses on the most anomaly-relevant-temporal regions through dynamic sparsification.<n>We publicly release frame-level spatial annotations and a method-agnostic evaluation for three public datasets.
  arXiv  Detail & Related papers  (2026-08-12T13:27:20Z)
• POST: Prior-Observation Adversarial Learning of Spatio-Temporal Associations for Multivariate Time Series Anomaly Detection [10.26613829763125]
  We propose a novel framework that unifies ad prior-observation adversarial learning paradigm.<n>In the spatial dimension, the model reconstructs the discrepancy between prior and data-driven observation in a minimax manner during training.<n>Such adversarial optimization not only improves the model sensitivity for time-wise detection, but also enables the model to localize anomalies to specific channels.
  arXiv  Detail & Related papers  (2026-05-18T09:34:14Z)
• CaC: Advancing Video Reward Models via Hierarchical Spatiotemporal Concentrating [49.94171749437024]
  We propose Concentrate and Concentrate (CaC), a coarse-to-fine anomaly reward model based on Vision-Language Models.<n>We construct the first largescale generated video anomaly dataset with per-frame bounding-box annotations, temporal anomaly windows, and fine-grained attribution labels.<n>In experiments, CaC can stably concentrate on subtle anomalies, achieving a 25.7% accuracy improvement on fine-grained anomaly benchmarks.
  arXiv  Detail & Related papers  (2026-05-12T08:08:33Z)
• Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts [57.01985221057047]
  This paper introduces a novel method that learnstemporal prompt embeddings for weakly supervised video anomaly detection and localization (WSVADL) based on pre-trained vision-language models (VLMs) Our method achieves state-of-theart performance on three public benchmarks for the WSVADL task.
  arXiv  Detail & Related papers  (2024-08-12T03:31:29Z)
• GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [68.14842693208465]
  GeneralAD is an anomaly detection framework designed to operate in semantic, near-distribution, and industrial settings. We propose a novel self-supervised anomaly generation module that employs straightforward operations like noise addition and shuffling to patch features. We extensively evaluated our approach on ten datasets, achieving state-of-the-art results in six and on-par performance in the remaining.
  arXiv  Detail & Related papers  (2024-07-17T09:27:41Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Geo-Adaptive Deep Spatio-Temporal predictive modeling for human mobility [5.864710987890994]
  Deep GA-vLS assumes data to be of fixed and regular shaped tensor shaped and face challenges of handling irregular data. We present a novel geo-aware enabled learning operation based on a novel data structure for dependencies while maintaining the recurrent mechanism.
  arXiv  Detail & Related papers  (2022-11-27T16:51:28Z)
• Spatio-temporal predictive tasks for abnormal event detection in videos [60.02503434201552]
  We propose new constrained pretext tasks to learn object level normality patterns. Our approach consists in learning a mapping between down-scaled visual queries and their corresponding normal appearance and motion characteristics. Experiments on several benchmark datasets demonstrate the effectiveness of our approach to localize and track anomalies.
  arXiv  Detail & Related papers  (2022-10-27T19:45:12Z)
• Unsupervised Domain Adaptation for Spatio-Temporal Action Localization [69.12982544509427]
  S-temporal action localization is an important problem in computer vision. We propose an end-to-end unsupervised domain adaptation algorithm. We show that significant performance gain can be achieved when spatial and temporal features are adapted separately or jointly.
  arXiv  Detail & Related papers  (2020-10-19T04:25:10Z)
• Localizing Anomalies from Weakly-Labeled Videos [45.58643708315132]
  We propose a WeaklySupervised Anomaly localization (WSAL) method focusing on temporally localizing anomalous segments within anomalous videos. Inspired by the appearance difference in anomalous videos, the evolution of adjacent temporal segments is evaluated for the localization of anomalous segments. Our proposed method achieves new state-of-the-art performance on the UCF-Crime and TAD datasets.
  arXiv  Detail & Related papers  (2020-08-20T12:58:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.