Single Frame

Single-frame processing in computer vision aims to extract meaningful information and perform complex tasks using only a single image from a video sequence, overcoming the limitations and delays associated with multi-frame approaches. Current research focuses on leveraging advanced architectures like Transformers and Graph Neural Networks, along with techniques such as contrastive learning and temporal attention, to achieve performance comparable to multi-frame methods in applications ranging from video denoising and anomaly detection to human pose estimation and video generation. This focus on single-frame processing is significant because it enables real-time applications and reduces computational costs, impacting diverse fields including medical imaging, autonomous systems, and assistive technologies for the visually impaired.