  Humans can easily segment moving objects without knowing what they are. That
objectness could emerge from continuous visual observations motivates us to
model grouping and movement concurrently from unlabeled videos. Our premise is
that a video has different views of the same scene related by moving
components, and the right region segmentation and region flow would allow
mutual view synthesis which can be checked from the data itself without any
external supervision. Our model starts with two separate pathways: an
appearance pathway that outputs feature-based region segmentation for a single
image, and a motion pathway that outputs motion features for a pair of images.
It then binds them in a conjoint representation called segment flow that pools
flow offsets over each region and provides a gross characterization of moving
regions for the entire scene. By training the model to minimize view synthesis
errors based on segment flow, our appearance and motion pathways learn region
segmentation and flow estimation automatically without building them up from
low-level edges or optical flows respectively. Our model demonstrates the
surprising emergence of objectness in the appearance pathway, surpassing prior
works on zero-shot object segmentation from an image, moving object
segmentation from a video with unsupervised test-time adaptation, and semantic
image segmentation by supervised fine-tuning. Our work is the first truly
end-to-end zero-shot object segmentation from videos. It not only develops
generic objectness for segmentation and tracking, but also outperforms
prevalent image-based contrastive learning methods without augmentation
engineering.
