  We introduce a new architecture for unsupervised object-centric
representation learning and multi-object detection and segmentation, which uses
a translation-equivariant attention mechanism to predict the coordinates of the
objects present in the scene and to associate a feature vector to each object.
A transformer encoder handles occlusions and redundant detections, and a
convolutional autoencoder is in charge of background reconstruction. We show
that this architecture significantly outperforms the state of the art on
complex synthetic benchmarks.
