Abstract
Video Instance Segmentation (VIS) faces significant annotation challenges due to its dual requirements of pixel-level masks and temporal consistency labels. While recent unsupervised methods like VideoCutLER eliminate optical flow dependencies through synthetic data, they remain constrained by the synthetic-to-real domain gap. We present AutoQ-VIS, a novel unsupervised framework that bridges this gap through quality-guided self-training. Our approach establishes a closed-loop system between pseudo-label generation and automatic quality assessment, enabling progressive adaptation from synthetic to real videos. Experiments demonstrate state-of-the-art performance with 52.6 $\text{AP}_{50}$ on YouTubeVIS-2019 val set, surpassing the previous state-of-the-art VideoCutLER by 4.4$\%$, while requiring no human annotations. This demonstrates the viability of quality-aware self-training for unsupervised VIS. The source code of our method is available at this https URL.
Abstract (translated)
视频实例分割（VIS）面临着像素级掩码和时间一致性标签的双重标注挑战。尽管最近的一些无监督方法，如VideoCutLER，通过合成数据消除了对光流依赖的需求，但它们仍然受限于从合成域到真实域的差距。我们提出了AutoQ-VIS，这是一种新的无监督框架，通过质量导向的自我训练来弥合这一差距。我们的方法在伪标签生成和自动质量评估之间建立了闭循环系统，从而能够逐步适应从合成视频到真实视频的过程。实验表明，在YouTubeVIS-2019验证集上，AutoQ-VIS达到了52.6%的$\text{AP}_{50}$成绩，比之前的最佳方法VideoCutLER高出4.4%，且无需人工标注。这证明了质量感知自我训练在无监督视频实例分割中的可行性。我们的方法源代码可在以下网址获得：[https URL]。 （提示：请将上述URL部分的实际链接地址替换为正确的项目网址以获取完整信息）
URL
https://arxiv.org/abs/2508.19808
