Referring Video Object Segmentation

Referring video object segmentation (RVOS) aims to identify and segment a specific object within a video based on a natural language description. Current research heavily utilizes transformer-based architectures, often incorporating techniques like multi-modal fusion, temporal consistency modeling, and efficient adaptation from pre-trained models (e.g., Segment Anything Model) to improve accuracy and reduce computational demands. This field is significant because it bridges computer vision and natural language processing, enabling more intuitive and robust video analysis for applications such as video editing, autonomous driving, and assistive technologies. Recent work also emphasizes handling challenging scenarios like limited annotations and semantic mismatches between descriptions and video content.