With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.

Dense multi-label action detection is a challenging task in the field of visual action, where multiple actions occur simultaneously in different time spans, hence accurately assessing the short-term and long-term temporal dependencies between actions is crucial for action detection. There is an urgent need for an effective temporal modeling technology to detect the temporal dependence of actions in videos and efficiently learn long-term and short-term action information. This paper proposes a new method based on temporal pyramid and long short-term time modeling for multi-label action detection, which combines hierarchical structure with pyramid feature hierarchy for dense multi-label temporal action detection. By using the expansion and compression convolution module (SEC) and external attention for time modeling, we focus on the temporal relationships of long and short-term actions at each stage. We then integrate hierarchical pyramid features to achieve accurate detection of actions at different temporal resolution scales. We evaluated the performance of the model on dense multi-label benchmark datasets, and achieved mAP of 47.3% and 36.0% on the MultiTHUMOS and TSU datasets, which outperforms 2.7% and 2.3% on the current state-of-the-art results. The code is available at https://github.com/Yoona6371/TP-LSM.