Video object segmentation quality assessment without groundtruth

Despite recent advances in Video Object Segmentation (VOS), state-of-the-art models still face challenges with occlusion, fast motion, and long-term tracking. These difficulties often result in a noticeable degradation of segmentation accuracy as the video progresses, leading to poor performance over extended periods. To tackle these persistent issues, we propose an innovative approach that enhances video segmentation by introducing predictive segmentation heads. Building upon the Cutie model, our method enables the model to predict Intersection over Union (IoU) results without ground truth, thereby enhancing video segmentation performance in challenging scenarios.