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.

本文提出了第一个完全无监督的方法，用于在真实场景的序列中分割多个对象，通过空间绑定对象并将这些槽联系起来，在高级语义特征空间中重建中间帧，解决了效率和规范化问题，成功地在 YouTube 视频中分割了复杂且多变的类别的多个实例。