2022/01/26 by Kateryna Chumachenko, Chumachenko, Kateryna, Alexandros Iosifidis +3
Computer Science · #Anomaly Detection Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Human Pose and Action Recognition #Image Retrieval and Classification Techniques
paper · pdf · doi:10.48550/arxiv.2201.11092
openalex publication_date 2022/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03
In this work, we propose several attention formulations for multivariate sequence data. We build on top of the recently introduced 2D-Attention and reformulate the attention learning methodology by quantifying the relevance of feature/temporal dimensions through latent spaces based on self-attention rather than learning them directly. In addition, we propose a joint feature-temporal attention mechanism that learns a joint 2D attention mask highlighting relevant information without treating feature and temporal representations independently. The proposed approaches can be used in various architectures and we specifically evaluate their application together with Neural Bag of Features feature extraction module. Experiments on several sequence data analysis tasks show the improved performance yielded by our approach compared to standard methods.