Semi Supervised Video Object Segmentation

Semi-supervised video object segmentation (VOS) aims to segment objects in a video sequence given only annotations for the first frame, a challenging task crucial for various applications. Current research heavily focuses on memory-based methods, often employing transformer architectures and incorporating optical flow information to improve temporal consistency and accuracy, particularly in handling occlusions and complex scenes. These advancements are driving progress towards real-time performance and efficient handling of long videos, impacting fields like autonomous driving, video editing, and animation.