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Language-Mediated, Object-Centric Representation Learning Language-Mediated, Object-Centric Representation Learning ACL 2021 Findings Ruocheng Wang * 1 Jiayuan Mao * 2 Samuel J. Gershman † 3 Jiajun Wu † 1 1 Stanford University 2 MIT CSAIL 3 Harvard University * and † indicate equal contribution [Paper] [BibTeX] Abstract 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,...
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