CADE: 基于集成的持续弱监督视频异常检测
CADE: Continual Weakly-supervised Video Anomaly Detection with Ensembles
AI总结：
CADE通过结合持续学习和弱监督方法，解决视频异常检测中的数据域变化和遗忘问题，提升检测性能。
摘要：
视频异常检测（VAD）长期以来被视为公共安全和犯罪预防中的关键问题。近年来，弱监督VAD（WVAD）因其易于标注过程和有前景的研究成果而受到广泛关注。尽管现有的WVAD方法主要针对静态数据集，但数据域可能变化的可能性被忽视了。为了适应这种领域转移，需要持续学习（CL）的视角，否则仅用新数据进行训练可能会导致先前数据性能下降，即遗忘。因此，我们提出了一种新的方法，称为基于集成的持续异常检测（CADE），这是首个将CL和WVAD视角结合的工作。具体而言，CADE使用双生成器（DG）来解决WVAD中的数据不平衡和标签不确定性问题。我们还发现，遗忘会加剧“不完整性”，即模型会偏向某些异常模式，导致各种异常的漏检。为了解决这个问题，我们提出使用多个模型的多判别器（MD）来捕捉由于遗忘而在过去场景中被遗漏的异常。大量实验表明，CADE在常见的多场景VAD数据集上，如ShanghaiTech和Charlotte异常数据集上，显著优于现有的VAD方法。
英文摘要：
Video anomaly detection (VAD) has long been studied as a crucial problem in public security and crime prevention. In recent years, weakly-supervised VAD (WVAD) have attracted considerable attention due to their easy annotation process and promising research results. While existing WVAD methods tackle mainly on static datasets, the possibility that the domain of data can vary has been neglected. To adapt such domain-shift, the continual learning (CL) perspective is required because otherwise additional training only with new coming data could easily cause performance degradation for previous data, i.e., forgetting. Therefore, we propose a brand-new approach, called Continual Anomaly Detection with Ensembles (CADE) that is the first work combining CL and WVAD viewpoints. Specifically, CADE uses the Dual-Generator(DG) to address data imbalance and label uncertainty in WVAD. We also found that forgetting exacerbates the "incompleteness'' where the model becomes biased towards certain anomaly modes, leading to missed detections of various anomalies. To address this, we propose to ensemble Multi-Discriminator (MD) that capture missed anomalies in past scenes due to forgetting, using multiple models. Extensive experiments show that CADE significantly outperforms existing VAD methods on the common multi-scene VAD datasets, such as ShanghaiTech and Charlotte Anomaly datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
