  We introduce a method to segment the visual field into independently moving
regions, trained with no ground truth or supervision. It consists of an
adversarial conditional encoder-decoder architecture based on Slot Attention,
modified to use the image as context to decode optical flow without attempting
to reconstruct the image itself. In the resulting multi-modal representation,
one modality (flow) feeds the encoder to produce separate latent codes (slots),
whereas the other modality (image) conditions the decoder to generate the first
(flow) from the slots. This design frees the representation from having to
encode complex nuisance variability in the image due to, for instance,
illumination and reflectance properties of the scene. Since customary
autoencoding based on minimizing the reconstruction error does not preclude the
entire flow from being encoded into a single slot, we modify the loss to an
adversarial criterion based on Contextual Information Separation. The resulting
min-max optimization fosters the separation of objects and their assignment to
different attention slots, leading to Divided Attention, or DivA. DivA
outperforms recent unsupervised multi-object motion segmentation methods while
tripling run-time speed up to 104FPS and reducing the performance gap from
supervised methods to 12% or less. DivA can handle different numbers of objects
and different image sizes at training and test time, is invariant to
permutation of object labels, and does not require explicit regularization.
