Learning to Compose: Improving Object Centric Learning by Injecting Compositionality

arXiv:2405.00646v1 Announce Type: cross
Abstract: Learning compositional representation is a key aspect of object-centric learning as it enables flexible systematic generalization and supports complex visual reasoning. However, most of the existing approaches rely on auto-encoding objective, while the compositionality is implicitly imposed by the architectural or algorithmic bias in the encoder. This misalignment between auto-encoding objective and learning compositionality often results in failure of capturing meaningful object representations. In this study, we propose a novel objective that explicitly encourages compositionality …

abstract algorithmic bias arxiv auto bias cs.cv cs.lg encoding however improving key object reasoning representation type visual while