Stream Encoder

Stream encoders are neural network architectures designed to process multiple data streams simultaneously, often combining different modalities like images and text or incorporating complementary information such as pose and RGB data from videos. Current research focuses on improving feature representation through techniques like attention mechanisms (e.g., pyramid attention, cross-gloss attention) and integrating transformer-based architectures for enhanced contextual understanding and cross-modal alignment. These advancements are significantly impacting fields like medical image registration, sign language retrieval, and video question answering by enabling more accurate and efficient analysis of complex data, leading to improved performance in various applications.