Vision-language models have the potential to enrich purely visual tasks by utilizing the combined representation of images/videos and corresponding textual descriptions. Recent advances in video anomaly detection have also integrated textual information to enhance the understanding of abnormal events. However, existing approaches often merge visual and textual modalities in a straightforward, bottom-up manner, failing to fully explore their interconnections. Moreover, textual captions themselves do not inherently convey “abnormal” attributes. Consequently, these joint representations tend to highlight all salient input features without adequately focusing on high-level tasks such as video anomaly detection. To direct the model’s attention towards anomalies more effectively, we propose incorporating a top-down mechanism into weakly supervised video anomaly detection tasks. A new Knowledge Sharing and Feedback (KSF) framework is designed to unify the representation of anomalies across both video and text. Specifically, we develop a category pattern sharing module that performs knowledge matching, acting as an alignment bridge between abnormal events and their corresponding descriptions. This ensures consistent representations for identical anomalies while maintaining distinct representations for different ones. Following this alignment process, matched high-level semantic priors are fed back into the forward path to enhance differentiation between abnormal and normal patterns. Comprehensive experiments on three benchmark datasets demonstrate the superiority of our proposed method in learning the implicit definition of anomaly patterns. The code is available at https://github.com/XJ-Cai/KSF.

Video anomaly detection plays a significant role in intelligent surveillance systems. To enhance model's anomaly recognition ability, previous works have typically involved RGB, optical flow, and text features. Recently, dynamic vision sensors (DVS) have emerged as a promising technology, which capture visual information as discrete events with a very high dynamic range and temporal resolution. It reduces data redundancy and enhances the capture capacity of moving objects compared to conventional camera. To introduce this rich dynamic information into the surveillance field, we created the first DVS video anomaly detection benchmark, namely UCF-Crime-DVS. To fully utilize this new data modality, a multi-scale spiking fusion network (MSF) is designed based on spiking neural networks (SNNs). This work explores the potential application of dynamic information from event data in video anomaly detection. Our experiments demonstrate the effectiveness of our framework on UCF-Crime-DVS and its superior performance compared to other models, establishing a new baseline for SNN-based weakly supervised video anomaly detection.

Recently, Spiking Neural Networks (SNNs) have demonstrated rich potential in computer vision domain due to their high biological plausibility, event-driven characteristic and energy-saving efficiency. Still, limited annotated event-based datasets and immature SNN architectures result in their performance inferior to that of Artificial Neural Networks (ANNs). To enhance the performance of SNNs on their optimal data format, DVS data, we explore using RGB data and well-performing ANNs to implement knowledge distillation. In this case, solving cross-modality and cross-architecture challenges is necessary. In this paper, we propose cross knowledge distillation (CKD), which not only leverages semantic similarity and sliding replacement to mitigate the cross-modality challenge, but also uses an indirect phased knowledge distillation to mitigate the cross-architecture challenge. We validated our method on main-stream neuromorphic datasets, including N-Caltech101 and CEP-DVS. The experimental results show that our method outperforms current State-of-the-Art methods. The code will be available at https://github.com/ShawnYE618/CKD.