Skip and Recover

"Skip and recover" strategies in various fields aim to improve efficiency and performance by selectively processing only the most relevant information. Current research focuses on applying this concept through intelligent skipping algorithms in computer vision (using reconfigurable CMOS sensors and attention mechanisms), deep learning (optimizing network architectures like Transformers and UNets), and other domains like speech recognition and scheduling. These methods show promise in reducing computational costs, improving accuracy, and enabling real-time processing in applications ranging from autonomous driving to medical image analysis and solar power forecasting.