Semantic Aggregation

Semantic aggregation in computer vision focuses on effectively combining information from multiple sources, such as frames in a video or points in a point cloud, to improve the accuracy and efficiency of tasks like video retrieval, semantic segmentation, and video restoration. Current research emphasizes developing novel aggregation methods, often incorporating attention mechanisms or specialized modules (e.g., temporal aggregation networks, excitation-aggregation designs) to handle diverse data types and address challenges like sparsity or computational cost. These advancements are driving improvements in various applications, including automated surgical skill assessment, deepfake detection, and compressed video enhancement, by enabling more robust and informative representations of complex visual data.