The recent works on Video Object Segmentation achieved remarkable results by
matching dense semantic and instance-level features between the current and
previous frames for long-time propagation. Nevertheless, global feature
matching ignores scene motion context, failing to satisfy temporal consistency.
Even though some methods introduce local matching branch to achieve smooth
propagation, they fail to model complex appearance changes due to the
constraints of the local window. In this paper, we present DeVOS (Deformable
VOS), an architecture for Video Object Segmentation that combines memory-based
matching with motion-guided propagation resulting in stable long-term modeling
and strong temporal consistency. For short-term local propagation, we propose a
novel attention mechanism ADVA (Adaptive Deformable Video Attention), allowing
the adaption of similarity search region to query-specific semantic features,
which ensures robust tracking of complex shape and scale changes. DeVOS employs
an optical flow to obtain scene motion features which are further injected to
deformable attention as strong priors to learnable offsets. Our method achieves
top-rank performance on DAVIS 2017 val and test-dev (88.1%, 83.0%), YouTube-VOS
2019 val (86.6%) while featuring consistent run-time speed and stable memory
consumption

结合基于记忆的匹配和运动引导传播的 DeVOS（可变形视频对象分割）架构，用于视频对象分割，实现稳定的长期建模和强大的时间一致性。