Video Decomposition

Video decomposition aims to separate a video into constituent elements, such as moving objects and background, enabling tasks like video editing and object tracking. Current research focuses on developing neural network models, including those employing layer-based representations, generative models with object-centric features, and working memory-inspired architectures, to achieve robust decomposition even in complex scenes with occlusions and noise. These advancements leverage techniques like hashing, transformers, and recurrent neural networks to improve efficiency and accuracy. The resulting decomposed videos find applications in various fields, including computer vision, medical imaging, and video editing software.