Dual Branch

Dual-branch architectures are a prevalent approach in various computer vision and machine learning tasks, aiming to improve model performance by processing data through parallel, often complementary, pathways. Current research focuses on leveraging these architectures for tasks such as image manipulation detection, medical image analysis, and speech enhancement, often incorporating advanced techniques like attention mechanisms, contrastive learning, and self-supervised pre-training to enhance feature extraction and model robustness. The widespread adoption of dual-branch models highlights their effectiveness in handling complex data and achieving state-of-the-art results across diverse applications, impacting fields ranging from healthcare to autonomous driving.