Self-supervised Object-Centric Learning for Videos
Görkay Aydemir Weidi Xie Fatma Güney
Unsupervised multi-object segmentation has shown impressive results on images
by utilizing powerful semantics learned from self-supervised pretraining. An
additional modality such as depth or motion is often used to facilitate the
segmentation in video sequences. However, the performance improvements observed
in synthetic sequences, which rely on the robustness of an additional cue, do
not translate to more challenging real-world scenarios. In this paper, we
propose the first fully unsupervised method for segmenting multiple objects in
real-world sequences. Our object-centric learning framework spatially binds
objects to slots on each frame and then relates these slots across frames. From
these temporally-aware slots, the training objective is to reconstruct the
middle frame in a high-level semantic feature space. We propose a masking
strategy by dropping a significant portion of tokens in the feature space for
efficiency and regularization. Additionally, we address over-clustering by
merging slots based on similarity. Our method can successfully segment multiple
instances of complex and high-variety classes in YouTube videos.