STCN presents a framework for modeling space-time correspondences in video object segmentation. The approach uses neural networks to track and segment objects across video frames by learning correspondence relationships in space and time. Built with PyTorch, the model achieves competitive accuracy on benchmarks like DAVIS 2017 and YouTubeVOS while running at real-time speeds of 20+ FPS. This is a research implementation with code for training and evaluation.