Semantic Anchor-Aligned Model for Interpretable Video Anomaly Detection under Cross-Modal Weak Supervision

Weakly supervised video anomaly detection is a key technique that relies solely on video-level labels to identify anomalous events. However, traditional multiple instance learning (MIL) methods rely on coarse-grained binary supervision. This approach makes it difficult to distinguish between fine-grained anomaly categories. These methods often focus solely on the most anomalous segments, resulting in detection outcomes that lack interpretability. To overcome these limitations, this study proposes a feature modelling approach that incorporates structured knowledge. By utilizing a dynamic semantic guidance mechanism, weakly supervised video anomaly detection combines external category-level information with learnable prompts to generate semantic signals within the feature space. These signals are aligned with the visual evidence extracted by the base anomaly detection module, producing two complementary outputs: an anomaly score for quantifying the severity of anomalous events, and semantic descriptions aligned with external concepts, which can be used to generate structured and interpretable explanatory texts through predefined templates. Experimental results demonstrate that the proposed method achieves an AUC of 88.03% on the UCF-Crime dataset and 98.23% on the ShanghaiTech dataset, attaining 87.05% accuracy in fine-grained anomaly classification tasks. Moreover, the generated semantic explanations extend weakly supervised detection from a binary classification task to a semantics-driven, interpretable anomaly analysis framework.