  Unsupervised video object segmentation (VOS) aims to detect and segment the
most salient object in videos. The primary techniques used in unsupervised VOS
are 1) the collaboration of appearance and motion information; and 2) temporal
fusion between different frames. This paper proposes two novel prototype-based
attention mechanisms, inter-modality attention (IMA) and inter-frame attention
(IFA), to incorporate these techniques via dense propagation across different
modalities and frames. IMA densely integrates context information from
different modalities based on a mutual refinement. IFA injects global context
of a video to the query frame, enabling a full utilization of useful properties
from multiple frames. Experimental results on public benchmark datasets
demonstrate that our proposed approach outperforms all existing methods by a
substantial margin. The proposed two components are also thoroughly validated
via ablative study.
