GMIS: Marrying Grounding-DINO and Motion Iterative Segment Anything Model for Referring Video Object Segmentation

Referring Video Object Segmentation (RVOS) aims to segment objects in videos based on natural language descriptions, which requires accurate spatial localization and temporal consistency. In this work, we propose a novel framework for robust video object segmentation by modifying the textual backbone of Grounding DINO in combination with an enhanced SAM2 model integrated with Iterative Kalman Filtering (IKF). Our approach leverages the open-ended lexical detection capabilities of Grounding DINO to recognize target objects described by natural language, while IKF-enhanced SAM2 ensures temporal consistency and accurate segmentation across video frames. With extensive experiments on Mevis and other datasets, our approach performs well in challenging scenarios with occlusion, fast motion, and complex scene changes while maintaining performance.