Two Stream

Two-stream networks are a prominent deep learning architecture designed to process data from two distinct sources, often spatial and temporal information, to improve performance in various tasks. Current research focuses on applying this architecture to diverse applications, including action recognition in videos, medical image analysis (e.g., anomaly detection, disease prediction), and other domains like music demixing and dental implant prediction, often incorporating convolutional neural networks (CNNs) and transformers. The effectiveness of this dual-stream approach stems from its ability to leverage complementary information, leading to improved accuracy and robustness compared to single-stream methods, with significant implications for fields requiring efficient and accurate analysis of complex data.