Related papers: Learning to Compose: Improving Object Centric Learning by Injecting Compositionality

Learning to Compose: Improving Object Centric Learning by Injecting Compositionality

URL: http://arxiv.org/abs/2405.00646v1
Date: Wed, 1 May 2024 17:21:36 GMT
Title: Learning to Compose: Improving Object Centric Learning by Injecting Compositionality
Authors: Whie Jung, Jaehoon Yoo, Sungjin Ahn, Seunghoon Hong,
Abstract summary: compositional representation is a key aspect of object-centric learning. Most of the existing approaches rely on auto-encoding objective. We propose a novel objective that explicitly encourages compositionality of the representations.
Score: 27.364435779446072
License: http://creativecommons.org/licenses/by/4.0/
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 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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