Temporal Shift Module

Temporal Shift Modules (TSMs) are techniques used in various deep learning applications to efficiently incorporate temporal information from sequential data like video or audio. Current research focuses on integrating TSMs into diverse architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and vision transformers (ViTs), to improve performance in tasks such as action recognition, speech processing, and micro-expression analysis. These modules enhance temporal modeling capabilities without significantly increasing computational complexity, leading to improved accuracy and efficiency in numerous applications. The impact of TSMs is evident in the advancement of state-of-the-art results across multiple domains, demonstrating their value as a powerful tool for processing time-series data.