Dual Stream

Dual-stream approaches in computer vision and related fields leverage the power of parallel processing by employing two distinct streams to extract complementary information from input data. Current research focuses on optimizing these streams for specific tasks, often using deep learning architectures like convolutional neural networks and transformers, sometimes incorporating handcrafted features or attention mechanisms to enhance performance. This strategy improves accuracy and efficiency in various applications, including image manipulation detection, action recognition, and medical image analysis, by enabling more robust and comprehensive feature extraction than single-stream methods. The resulting advancements contribute to improved model performance and broader applicability across diverse domains.