  In this paper, we propose a simple yet effective approach for self-supervised
video object segmentation (VOS). Our key insight is that the inherent
structural dependencies present in DINO-pretrained Transformers can be
leveraged to establish robust spatio-temporal correspondences in videos.
Furthermore, simple clustering on this correspondence cue is sufficient to
yield competitive segmentation results. Previous self-supervised VOS techniques
majorly resort to auxiliary modalities or utilize iterative slot attention to
assist in object discovery, which restricts their general applicability and
imposes higher computational requirements. To deal with these challenges, we
develop a simplified architecture that capitalizes on the emerging objectness
from DINO-pretrained Transformers, bypassing the need for additional modalities
or slot attention. Specifically, we first introduce a single spatio-temporal
Transformer block to process the frame-wise DINO features and establish
spatio-temporal dependencies in the form of self-attention. Subsequently,
utilizing these attention maps, we implement hierarchical clustering to
generate object segmentation masks. To train the spatio-temporal block in a
fully self-supervised manner, we employ semantic and dynamic motion consistency
coupled with entropy normalization. Our method demonstrates state-of-the-art
performance across multiple unsupervised VOS benchmarks and particularly excels
in complex real-world multi-object video segmentation tasks such as
DAVIS-17-Unsupervised and YouTube-VIS-19. The code and model checkpoints will
be released at https://github.com/shvdiwnkozbw/SSL-UVOS.
