  In this paper, we introduce a self-supervised approach for video object
segmentation without human labeled data.Specifically, we present Robust
Pixel-level Matching Net-works (RPM-Net), a novel deep architecture that
matches pixels between adjacent frames, using only color information from
unlabeled videos for training. Technically, RPM-Net can be separated in two
main modules. The embed-ding module first projects input images into high
dimensional embedding space. Then the matching module with deformable
convolution layers matches pixels between reference and target frames based on
the embedding features.Unlike previous methods using deformable convolution,
our matching module adopts deformable convolution to focus on similar features
in spatio-temporally neighboring pixels.Our experiments show that the selective
feature sampling improves the robustness to challenging problems in video
object segmentation such as camera shake, fast motion, deformation, and
occlusion. Also, we carry out comprehensive experiments on three public
datasets (i.e., DAVIS-2017,SegTrack-v2, and Youtube-Objects) and achieve
state-of-the-art performance on self-supervised video object seg-mentation.
Moreover, we significantly reduce the performance gap between self-supervised
and fully-supervised video object segmentation (41.0% vs. 52.5% on DAVIS-2017
validation set)
