  We present Language-mediated, Object-centric Representation Learning (LORL),
a paradigm for learning disentangled, object-centric scene representations from
vision and language. LORL builds upon recent advances in unsupervised object
discovery and segmentation, notably MONet and Slot Attention. While these
algorithms learn an object-centric representation just by reconstructing the
input image, LORL enables them to further learn to associate the learned
representations to concepts, i.e., words for object categories, properties, and
spatial relationships, from language input. These object-centric concepts
derived from language facilitate the learning of object-centric
representations. LORL can be integrated with various unsupervised object
discovery algorithms that are language-agnostic. Experiments show that the
integration of LORL consistently improves the performance of unsupervised
object discovery methods on two datasets via the help of language. We also show
that concepts learned by LORL, in conjunction with object discovery methods,
aid downstream tasks such as referring expression comprehension.
