Video anomaly detection (VAD) is an important computer vision problem. Thanks to the mode coverage capabilities of generative models, the likelihood-based paradigm is catching growing interest, as it can model normal distribution and detect out-of-distribution anomalies. However, these likelihood-based methods are blind to the anomalies located in local modes near the learned distribution. To handle these ``unseen" anomalies, we dive into three gaps uniquely existing in VAD regarding scene, motion and appearance. Specifically, we first build a noise-conditioned score transformer for denoising score matching. Then, we introduce a scene-dependent and motion-aware score function by embedding the scene condition of input sequences into our model and assigning motion weights based on the difference between key frames of input sequences. Next, to solve the problem of blindness in principle, we integrate unaffected visual information via a novel autoregressive denoising score matching mechanism for inference. Through autoregressively injecting intensifying Gaussian noise into the denoised data and estimating the corresponding score function, we compare the denoised data with the original data to get a difference and aggregate it with the score function for an enhanced appearance perception and accumulate the abnormal context. With all three gaps considered, we can compute a more comprehensive anomaly indicator. Experiments on three popular VAD benchmarks demonstrate the state-of-the-art performance of our method.

Video anomaly detection (VAD) plays a crucial role in intelligent surveillance. However, an essential type of anomaly named scene-dependent anomaly is overlooked. Moreover, the task of video anomaly anticipation (VAA) also deserves attention. To fill these gaps, we build a comprehensive dataset named NWPU Campus, which is the largest semi-supervised VAD dataset and the first dataset for scene-dependent VAD and VAA. Meanwhile, we introduce a novel forward-backward framework for scene-dependent VAD and VAA, in which the forward network individually solves the VAD and jointly solves the VAA with the backward network. Particularly, we propose a scene-dependent generative model in latent space for the forward and backward networks. First, we propose a hierarchical variational auto-encoder to extract scene-generic features. Next, we design a score-based diffusion model in latent space to refine these features more compact for the task and generate scene-dependent features with a scene information auto-encoder, modeling the relationships between video events and scenes. Finally, we develop a temporal loss from key frames to constrain the motion consistency of video clips. Extensive experiments demonstrate that our method can handle both scene-dependent anomaly detection and anticipation well, achieving state-of-the-art performance on ShanghaiTech, CUHK Avenue, and the proposed NWPU Campus datasets.