  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
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 segmentation algorithms that are
language-agnostic. Experiments show that the integration of LORL consistently
improves the object segmentation performance of MONet and Slot Attention on two
datasets via the help of language. We also show that concepts learned by LORL,
in conjunction with segmentation algorithms such as MONet, aid downstream tasks
such as referring expression comprehension.
