Multi Frame

Multi-frame processing leverages information from multiple sequential frames in video or image sequences to improve accuracy and efficiency over single-frame methods. Current research focuses on applying this technique across diverse computer vision tasks, including 3D object tracking, scene understanding, optical flow estimation, and video restoration, often employing transformer-based networks or convolutional neural networks with specialized modules for temporal information aggregation and efficient processing. These advancements lead to significant improvements in accuracy and speed for applications ranging from autonomous driving and medical image analysis to historical video restoration and augmented reality. The resulting enhanced performance and efficiency are driving significant progress in various fields.