Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
arxiv(2024)
摘要
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 of the
representations. Built upon the existing object-centric learning framework
(e.g., slot attention), our method incorporates additional constraints that an
arbitrary mixture of object representations from two images should be valid by
maximizing the likelihood of the composite data. We demonstrate that
incorporating our objective to the existing framework consistently improves the
objective-centric learning and enhances the robustness to the architectural
choices.
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