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We introduce a method to segment the visual field into independently movingregions, trained with no ground truth or supervision. It consists of anadversarial conditional encoder-decoder architecture based on Slot Attention,modified to use the image as context to decode optical flow without attemptingto 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 toencode complex nuisance variability in the image due to, for instance,illumination and reflectance properties of the scene. Since customaryautoencoding based on minimizing the reconstruction error does not preclude theentire flow from being encoded into a single slot, we modify the loss to anadversarial criterion based on Contextual Information Separation. The resultingmin-max optimization fosters the separation of objects and their assignment todifferent attention slots, leading to Divided Attention, or DivA. DivAoutperforms recent unsupervised multi-object motion segmentation methods whiletripling run-time speed up to 104FPS and reducing the performance gap fromsupervised methods to 12% or less. DivA can handle different numbers of objectsand different image sizes at training and test time, is invariant topermutation of object labels, and does not require explicit regularization.

Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots