Video anomaly detection (VAD) is a challenging task that detects anomalous frames in continuous surveillance videos. Most previous work utilizes the spatio-temporal correlation of visual features to distinguish whether there are abnormalities in video snippets. Recently, some works attempt to introduce multi-modal information, like text feature, to enhance the results of video anomaly detection. However, these works merely incorporate text features into video snippets in a coarse manner, overlooking the significant amount of redundant information that may exist within the video snippets. Therefore, we propose to leverage the diversity among multi-modal information to further refine the extracted features, reducing the redundancy in visual features, and we propose Grained Multi-modal Feature for Video Anomaly Detection (GMFVAD). Specifically, we generate more grained multi-modal feature based on the video snippet, which summarizes the main content, and text features based on the captions of original video will be introduced to further enhance the visual features of highlighted portions. Experiments show that the proposed GMFVAD achieves state-of-the-art performance on four mainly datasets. Ablation experiments also validate that the improvement of GMFVAD is due to the reduction of redundant information.

翻译：视频异常检测（VAD）是一项具有挑战性的任务，旨在检测连续监控视频中的异常帧。先前的研究大多利用视觉特征的时空相关性来判别视频片段中是否存在异常。近年来，一些工作尝试引入文本特征等多模态信息以提升视频异常检测的效果。然而，这些工作仅以粗粒度方式将文本特征融入视频片段，忽略了视频片段内部可能存在的显著冗余信息。因此，我们提出利用多模态信息之间的差异性来进一步提炼提取的特征，减少视觉特征中的冗余，并提出了用于视频异常检测的细粒度多模态特征方法（GMFVAD）。具体而言，我们基于视频片段生成更细粒度的多模态特征以概括主要内容，同时引入基于原始视频描述生成的文本特征，以进一步增强视频关键部分的视觉特征。实验表明，所提出的GMFVAD在四个主流数据集上取得了最先进的性能。消融实验也验证了GMFVAD的性能提升源于冗余信息的减少。