  We describe an unsupervised method to detect and segment portions of images
of live scenes that, at some point in time, are seen moving as a coherent
whole, which we refer to as objects. Our method first partitions the motion
field by minimizing the mutual information between segments. Then, it uses the
segments to learn object models that can be used for detection in a static
image. Static and dynamic models are represented by deep neural networks
trained jointly in a bootstrapping strategy, which enables extrapolation to
previously unseen objects. While the training process requires motion, the
resulting object segmentation network can be used on either static images or
videos at inference time. As the volume of seen videos grows, more and more
objects are seen moving, priming their detection, which then serves as a
regularizer for new objects, turning our method into unsupervised continual
learning to segment objects. Our models are compared to the state of the art in
both video object segmentation and salient object detection. In the six
benchmark datasets tested, our models compare favorably even to those using
pixel-level supervision, despite requiring no manual annotation.
