  We propose a novel self-supervised Video Object Segmentation (VOS) approach
that strives to achieve better object-background discriminability for accurate
object segmentation. Distinct from previous self-supervised VOS methods, our
approach is based on a discriminative learning loss formulation that takes into
account both object and background information to ensure object-background
discriminability, rather than using only object appearance. The discriminative
learning loss comprises cutout-based reconstruction (cutout region represents
part of a frame, whose pixels are replaced with some constant values) and tag
prediction loss terms. The cutout-based reconstruction term utilizes a simple
cutout scheme to learn the pixel-wise correspondence between the current and
previous frames in order to reconstruct the original current frame with added
cutout region in it. The introduced cutout patch guides the model to focus as
much on the significant features of the object of interest as the less
significant ones, thereby implicitly equipping the model to address
occlusion-based scenarios. Next, the tag prediction term encourages
object-background separability by grouping tags of all pixels in the cutout
region that are similar, while separating them from the tags of the rest of the
reconstructed frame pixels. Additionally, we introduce a zoom-in scheme that
addresses the problem of small object segmentation by capturing fine structural
information at multiple scales. Our proposed approach, termed CT-VOS, achieves
state-of-the-art results on two challenging benchmarks: DAVIS-2017 and
Youtube-VOS. A detailed ablation showcases the importance of the proposed loss
formulation to effectively capture object-background discriminability and the
impact of our zoom-in scheme to accurately segment small-sized objects.
