Joint Cross Attention

Joint cross-attention is a technique used to integrate information from multiple data sources, enhancing the performance of machine learning models. Current research focuses on applying this approach to diverse fields, including multimodal emotion recognition (audio-visual fusion), human pose estimation, and longitudinal data analysis, often employing transformer-based architectures or incorporating them with convolutional neural networks. These advancements improve model accuracy and efficiency in various applications, from diagnosing autism spectrum disorder through robot-assisted therapy to improving the accuracy of 3D human modeling and emotion recognition systems. The resulting improvements in data analysis and model performance have significant implications across multiple scientific disciplines and practical applications.