Temporal Aggregation

Temporal aggregation focuses on effectively combining information across time in various data types, aiming to improve model performance and efficiency. Current research emphasizes developing novel architectures, such as incorporating meta-learning principles or utilizing transformer networks with tailored aggregation modules (e.g., late fusion, temporal deformable alignment), to handle the complexities of temporal data in diverse applications like video processing, EEG analysis, and autonomous driving. These advancements lead to improved accuracy and efficiency in tasks ranging from video semantic segmentation and object tracking to sequential recommendation and clinical EEG interpretation.