Learning Explicit Object-Centric Representations with Vision Transformers

Oscar Vikström; Alexander Ilin

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arXiv · 2210.14139

Learning Explicit Object-Centric Representations with Vision Transformers

Oscar Vikström·Alexander Ilin

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Abstract

With the recent successful adaptation of transformers to the vision domain, particularly when trained in a self-supervised fashion, it has been shown that vision transformers can learn impressive object-reasoning-like behaviour and features expressive for the task of object segmentation in images. In this paper, we build on the self-supervision task of masked autoencoding and explore its effectiveness for explicitly learning object-centric representations with transformers. To this end, we design an object-centric autoencoder using transformers only and train it end-to-end to reconstruct full images from unmasked patches. We show that the model efficiently learns to decompose simple scenes as measured by segmentation metrics on several multi-object benchmarks.

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Oscar Vikström, Alexander Ilin. 2022-10-25. Learning Explicit Object-Centric Representations with Vision Transformers. https://arxiv.org/abs/2210.14139

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