  Unsupervised video object segmentation has often been tackled by methods
based on recurrent neural networks and optical flow. Despite their complexity,
these kinds of approaches tend to favour short-term temporal dependencies and
are thus prone to accumulating inaccuracies, which cause drift over time.
Moreover, simple (static) image segmentation models, alone, can perform
competitively against these methods, which further suggests that the way
temporal dependencies are modelled should be reconsidered. Motivated by these
observations, in this paper we explore simple yet effective strategies to model
long-term temporal dependencies. Inspired by the non-local operators of [70],
we introduce a technique to establish dense correspondences between pixel
embeddings of a reference "anchor" frame and the current one. This allows the
learning of pairwise dependencies at arbitrarily long distances without
conditioning on intermediate frames. Without online supervision, our approach
can suppress the background and precisely segment the foreground object even in
challenging scenarios, while maintaining consistent performance over time. With
a mean IoU of $81.7\%$, our method ranks first on the DAVIS-2016 leaderboard of
unsupervised methods, while still being competitive against state-of-the-art
online semi-supervised approaches. We further evaluate our method on the FBMS
dataset and the ViSal video saliency dataset, showing results competitive with
the state of the art.
