Spatial Fusion

Spatial fusion in computer vision and signal processing aims to integrate information from multiple sources, such as different sensor modalities (e.g., RGB and depth images, LiDAR and camera data) or temporal frames, to create a more comprehensive and robust representation. Current research heavily utilizes transformer-based architectures and attention mechanisms to effectively fuse features at both channel and spatial levels, often incorporating hierarchical or cooperative attention strategies for improved performance. These advancements are significantly impacting various applications, including autonomous driving (enhanced object detection and scene understanding), speech emotion recognition, and salient object detection, by enabling more accurate and efficient processing of complex data.