  Semi-supervised video object segmentation (VOS) aims to densely track certain
designated objects in videos. One of the main challenges in this task is the
existence of background distractors that appear similar to the target objects.
We propose three novel strategies to suppress such distractors: 1) a
spatio-temporally diversified template construction scheme to obtain
generalized properties of the target objects; 2) a learnable distance-scoring
function to exclude spatially-distant distractors by exploiting the temporal
consistency between two consecutive frames; 3) swap-and-attach augmentation to
force each object to have unique features by providing training samples
containing entangled objects. On all public benchmark datasets, our model
achieves a comparable performance to contemporary state-of-the-art approaches,
even with real-time performance. Qualitative results also demonstrate the
superiority of our approach over existing methods. We believe our approach will
be widely used for future VOS research.
