Problem
Research questions and friction points this paper is trying to address.
weakly supervised video anomaly detection
spatial localization
background bias
ethical concerns
spatio-temporal reasoning
Localizing to Debias: A Patch-Level Benchmark and Baseline for Weakly Supervised Spatial Anomaly Detection
🤖 AI Summary
Weakly supervised video anomaly detection is often hindered by interference from background clutter and scene-level cues, leading to spatial bias and limited interpretability. To address this, this work proposes the SST-WSVAD framework, which dynamically sparsifies attention to focus on critical spatiotemporal regions and integrates end-to-end coupled spatial and temporal branches for fine-grained anomaly localization. The method innovatively introduces a motion-aware regularization mechanism that operates without external detectors or language prompts, enabling patch-level analysis of spatial bias and facilitating auditable evaluation. Evaluated on UCF-Crime, XD-Violence, and MSAD datasets, the approach achieves performance on par with state-of-the-art methods while offering fine-grained interpretability in both anomaly localization and scene-induced bias.
📝 Abstract
Despite growing interest in weakly supervised video anomaly detection (WSVAD), current methods struggle to bridge the gap between coarse temporal supervision and fine-grained spatial reasoning. A key obstacle is the tendency of temporal detectors to latch onto background and scene-level cues rather than truly discriminative anomaly evidence. This background bias raises ethical concerns: models may inadvertently associate anomalies with societal or environmental context rather than authentic crime-related cues. Without spatial grounding, such biases remain hidden and unauditable. To address this, we propose SST-WSVADL, a sparse spatio-temporal framework that bridges temporal anomaly detection with fine-grained spatial localization. Rather than processing all spatial regions indiscriminately, SST-WSVADL progressively focuses on the most anomaly-relevant spatio-temporal regions through dynamic sparsification, naturally suppressing background dominant content while preserving discriminative evidence. The temporal and spatial branches are coupled end-to-end via motion-aware regularization that guides sparsification toward dynamically informative regions, without relying on external detectors or vision-language prompts. We publicly release frame-level spatial annotations and a method-agnostic evaluation protocol for three public datasets: UCF-Crime, XD-Violence, and MSAD. These resources enable the community to audit spatial biases in WSVAD predictions, supporting progress toward more ethical and accountable anomaly detection. Experiments demonstrate that SST-WSVADL is competitive with prior methods across benchmarks while enabling localization and patch-level auditability of scene bias, providing a reproducible foundation for interpretability-oriented evaluation of WSVAD models.
Innovation
Methods, ideas, or system contributions that make the work stand out.
weakly supervised anomaly detection
spatio-temporal localization
dynamic sparsification
bias auditing
motion-aware regularization
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