Temporal Difference Transformer

Temporal Difference Transformers (TDTs) are a novel approach leveraging the power of transformer architectures to analyze temporal data, particularly in video analysis. Current research focuses on enhancing the ability of transformers to capture both local and global temporal information within video sequences, often by incorporating inter-frame difference features to highlight dynamic changes. This approach has shown promise in applications like video-text retrieval and remote physiological signal measurement (e.g., rPPG from facial videos), outperforming previous methods based on convolutional neural networks. The development of TDTs offers a powerful new tool for analyzing time-series data across various domains, potentially improving the accuracy and efficiency of numerous applications.