Residual Frame

Residual frames, representing the difference between consecutive frames in a video or the discrepancy between a model's prediction and the ground truth, are central to improving various computer vision and machine learning tasks. Current research focuses on leveraging residual information within sophisticated architectures like diffusion models, vector quantized neural networks, and transformer-based models to enhance video quality, improve motion prediction and generation, and enable efficient parameter-efficient fine-tuning. This focus on residuals is driving advancements in video compression, denoising, super-resolution, motion retargeting, and object tracking, ultimately leading to more robust and efficient algorithms across diverse applications.