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Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
May 2, 2024, 4:43 a.m. | Whie Jung, Jaehoon Yoo, Sungjin Ahn, Seunghoon Hong
cs.LG updates on arXiv.org arxiv.org
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
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