Frame Selection

Frame selection focuses on efficiently identifying the most informative subset of frames from a video or audio sequence for downstream tasks, improving both computational efficiency and model performance. Current research explores various methods, including those based on learned importance scores, graph attention networks, and heuristic search algorithms, often integrated into larger architectures for tasks like text-to-video retrieval, action recognition, and speech synthesis. These advancements are significant because they reduce computational costs associated with processing large multimedia datasets while maintaining or even improving accuracy in applications ranging from video understanding to personalized voice generation.