Paper Overview
- Field: Computer Vision (CV)
- Authors: Wenti Yin, Xiang Wang, Huaxin Zhang
- Published: 2026-08-12
- arXiv: 2508.05149
- Training-free VAD methods rely on pretrained models but lack an explicit criterion distinguishing hazard resemblance from true abnormality.
- CEAVAD reframes detection as contrastive adjudication between falsifiable hazard and benign hypotheses.
- Public safety knowledge supplies paired hazard/benign explanations for each hazard mechanism.
- Adjudication uses video evidence to confirm or refute hazard hypotheses, supporting both temporal localization and interpretability.
- State-of-the-art results on three standard VAD benchmarks in the training-free setting.
- arXiv: https://arxiv.org/abs/2508.05149
Summary
Video anomaly detection (VAD) aims to identify and temporally localize abnormal events in videos. Supervised methods learn anomaly decision boundaries from target-domain annotations but require substantial in-domain data. Existing training-free methods leverage the rich semantic knowledge and reasoning capabilities of pretrained models to interpret visual content, yet these capabilities do not directly define an anomaly decision criterion: richer anomaly descriptions better capture hazard resemblance without resolving abnormality.
To address this gap, the authors propose Contrastive Event Adjudication for training-free Video Anomaly Detection (CEAVAD), which shifts the unit of inference from isolated anomaly concepts to falsifiable event hypotheses and establishes an inference-time explanatory boundary through the interaction between competing explanations and video evidence.
Method
1. Hazard-normal event contrast: Using public safety knowledge, CEAVAD constructs hazard-normal contrasts by pairing each hazard mechanism with both a generic normal explanation and a mechanism-specific benign counterpart. 2. Contrastive boundary proposal: For a target video segment, the method assesses whether the segment more strongly supports the hazard explanation or its benign counterpart, producing a revisable contrastive boundary proposal. 3. Adjudication against evidence: CEAVAD adjudicates between competing explanations to determine whether the hazard hypothesis can withstand scrutiny from the video evidence, simultaneously supporting temporally localized anomaly detection and evidence-grounded explanation.
Results
Experiments on three widely used VAD benchmarks show that CEAVAD achieves state-of-the-art performance within the training-free paradigm, without requiring any in-domain labeled training data.