  Time Series Classification (TSC) is an important and challenging task for
many visual computing applications. Despite the extensive range of methods
developed for TSC, relatively few utilized Deep Neural Networks (DNNs). In this
paper, we propose two novel attention blocks (Global Temporal Attention and
Temporal Pseudo-Gaussian augmented Self-Attention) that can enhance deep
learning-based TSC approaches, even when such approaches are designed and
optimized for a specific dataset or task. We validate this claim by evaluating
multiple state-of-the-art deep learning-based TSC models on the University of
East Anglia (UEA) benchmark, a standardized collection of 30 Multivariate Time
Series Classification (MTSC) datasets. We show that adding the proposed
attention blocks improves base models' average accuracy by up to 3.6%.
Additionally, the proposed TPS block uses a new injection module to include the
relative positional information in transformers. As a standalone unit with less
computational complexity, it enables TPS to perform better than most of the
state-of-the-art DNN-based TSC methods. The source codes for our experimental
setups and proposed attention blocks are made publicly available.
