PeriodVOS: Learning Periodic Patterns for Unsupervised Video Object Segmentation via Adaptive Contextual Coupling

Existing Unsupervised Video Object Segmentation (UVOS) solutions primarily focus on frame-to-frame propagation and often struggle with extended sequences where objects undergo complex transformations. In this work, we observe that many natural and artificial motions exhibit inherent periodicity, where objects return to similar states across time, particularly in complex scenarios. Leveraging this insight, we present PeriodVOS, a novel framework that exploits recurring motion patterns to enhance segmentation quality across diverse video contexts. Specifically, we propose to establish intra-period consistency to enforce stable segmentation within short time windows, while mitigating the effects of temporary disturbances. Furthermore, to capture global dependencies, we present inter-period correlation to build associations between similar object states across different time periods. Additionally, an adaptive temporal contextual coupling is designed to dynamically adjust how temporal context is integrated based on video content. Through extensive evaluation on six standard benchmarks, including DAVIS-2016, FBMS, Youtube-Objects, DAVSOD, ViSal, and MCL datasets, our PeriodVOS outperforms state-of-thearts, demonstrating the potential of video periodic mining particularly in challenging scenarios. We have released source code on https://github.com/smdshzyjbr-qhw/PeriodVOS.