Boosting Unsupervised Video Instance Segmentation with Automatic Quality-Guided Self-Training
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 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. We will release the code at https://github.com/wcbup/AutoQ-VIS.
69394fefdfc35938ba12a212
Kaixuan Lu
69394fefdfc35938ba12a213
Mehmet Onurcan Kaya
admin_assigned
2025-12-10T15:44:47.317Z
63be9021da08ed0544f36c38
/avatars/42511d8b1d3a3ef99bc154c98b72dfba.svg
onurcan
monurcan
user
monurcan
69394fefdfc35938ba12a214
Dim P. Papadopoulos
Boosting Unsupervised Video Instance Segmentation with Automatic Quality-Guided Self-Training
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 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. We will release the code at https://github.com/wcbup/AutoQ-VIS.
AutoQ-VIS achieves state-of-the-art results in unsupervised Video Instance Segmentation using quality-guided self-training to bridge the synthetic-to-real domain gap.
unsupervised methods VideoCutLER quality-guided self-training closed-loop system pseudo-label generation automatic quality assessment YouTubeVIS-2019 AP50