Learning Explicit Object-Centric Representations with Vision Transformers

• URL: http://arxiv.org/abs/2210.14139v1
• Date: Tue, 25 Oct 2022 16:39:49 GMT
• Title: Learning Explicit Object-Centric Representations with Vision Transformers
• Authors: Oscar Vikstr\"om, Alexander Ilin
• Abstract summary: We build on the self-supervision task of masked autoencoding and explore its effectiveness for learning object-centric representations with transformers. We show that the model efficiently learns to decompose simple scenes as measured by segmentation metrics on several multi-object benchmarks.
• Score: 81.38804205212425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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