👉 Clips computing, also known as clip computing or clip-based computation, is a novel approach to machine learning that focuses on processing and learning from short video clips rather than individual frames. This technique involves breaking down videos into manageable segments, typically around 10-30 seconds long, which are then processed independently or in parallel. These clips are fed into machine learning models that can learn temporal dependencies and patterns across the entire sequence, enabling more efficient and effective training for tasks such as action recognition, video classification, and anomaly detection. By leveraging the temporal context provided by clips, clips computing can significantly reduce computational complexity and memory requirements compared to processing full videos, making it particularly suitable for real-time applications and resource-constrained environments.
