Learning Event Completeness for Weakly Supervised Video Anomaly Detection

• URL: http://arxiv.org/abs/2506.13095v1
• Date: Mon, 16 Jun 2025 04:56:58 GMT
• Title: Learning Event Completeness for Weakly Supervised Video Anomaly Detection
• Authors: Yu Wang, Shiwei Chen,
• Abstract summary: We present a novel Learning Event Completeness for Weakly Supervised Video Anomaly Detection (LEC-VAD)<n>LEC-VAD encodes both category-aware and category-agnostic semantics between vision and language.<n>We develop a novel memory bank-based prototype learning mechanism to enrich concise text descriptions associated with anomaly-event categories.
• Score: 5.140169437190526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Weakly supervised video anomaly detection (WS-VAD) is tasked with pinpointing temporal intervals containing anomalous events within untrimmed videos, utilizing only video-level annotations. However, a significant challenge arises due to the absence of dense frame-level annotations, often leading to incomplete localization in existing WS-VAD methods. To address this issue, we present a novel LEC-VAD, Learning Event Completeness for Weakly Supervised Video Anomaly Detection, which features a dual structure designed to encode both category-aware and category-agnostic semantics between vision and language. Within LEC-VAD, we devise semantic regularities that leverage an anomaly-aware Gaussian mixture to learn precise event boundaries, thereby yielding more complete event instances. Besides, we develop a novel memory bank-based prototype learning mechanism to enrich concise text descriptions associated with anomaly-event categories. This innovation bolsters the text's expressiveness, which is crucial for advancing WS-VAD. Our LEC-VAD demonstrates remarkable advancements over the current state-of-the-art methods on two benchmark datasets XD-Violence and UCF-Crime.

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