Video Anomaly Detection (VAD) involves detecting anomalous events in videos, presenting a significant and intricate task within intelligent video surveillance. Existing studies often concentrate solely on features acquired from limited normal data, disregarding the latent prior knowledge present in extensive natural image datasets. To address this constraint, we propose a Prior Knowledge Guided Network(PKG-Net) for the VAD task. First, an auto-encoder network is incorporated into a teacher-student architecture to learn two designated proxy tasks: future frame prediction and teacher network imitation, which can provide better generalization ability on unknown samples. Second, knowledge distillation on proper feature blocks is also proposed to increase the multi-scale detection ability of the model. In addition, prediction error and teacher-student feature inconsistency are combined to evaluate anomaly scores of inference samples more comprehensively. Experimental results on three public benchmarks validate the effectiveness and accuracy of our method, which surpasses recent state-of-the-arts.

The quality of user-generated reviews is significant for users to understand recommendation results and make online purchasing decisions correctly. However, the reliability of a review, which captures the likelihood that a review is benign, is ignored by many studies. The low reliability reviews cause a recommendation system's unsatisfying performance. Especially the fake reviews written by fraudulent users mislead the system into generating error recommendation results and explanations, which confuse customers and deprive customers of confidence in the system. In this paper, we propose a model, Reliable Recommendation with Review-level Explanations (RRRE), which detects reliable reviews and improves the performance of the explainable recommendation system as well. Recognizing the textual content of reviews, user-item interactions are valuable features for both rating prediction and reliability prediction. RRRE builds a uniform framework to predict rating scores and reliability scores simultaneously. Firstly, RRRE embeds user preferences and item profiles, which are extracted from textual and interactive features, into the representation of the review. Secondly, the supervised information of two subtasks is jointly combined. It makes the optimization of RRRE faster and better. Finally, the reviews with both high reliability scores and rating scores are given to customers as reliable explanations. To the best of our knowledge, we are the first to consider the reliability of reviews for improving explainable recommender system. And the experimental results confirm this idea and show that our model outperforms other baseline methods on Yelp and Amazon datasets.