Keyframe Selection

Keyframe selection aims to identify the most informative frames from a video or image sequence, optimizing data efficiency and improving downstream tasks. Current research focuses on developing efficient algorithms, often leveraging techniques like similarity measures (e.g., Wasserstein distance, text-video similarity), Gaussian mixture models, and incorporating keyframe selection into larger systems such as SLAM and video LLMs. These advancements are crucial for improving the performance and scalability of applications ranging from 3D reconstruction and augmented reality to large-scale video analysis and understanding.