Complex Video Object Segmentation

Complex video object segmentation (CVOS) aims to accurately identify and track objects throughout video sequences, even in challenging scenarios with occlusions, small objects, and crowded scenes. Recent research focuses on improving robustness to these complexities, often employing large language models to incorporate world knowledge and improve reasoning capabilities, as well as exploring novel architectures that leverage semantic understanding and motion information for more accurate segmentation. Advances in CVOS are crucial for applications such as video editing, autonomous driving, and human-computer interaction, driving the development of more sophisticated and robust computer vision systems.