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arXiv · 2012.04718

Canonical Capsules: Self-Supervised Capsules in Canonical Pose

Weiwei Sun·Andrea Tagliasacchi·Boyang Deng·Sara Sabour·Soroosh Yazdani·Geoffrey Hinton·Kwang Moo Yi
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Abstract

We propose a self-supervised capsule architecture for 3D point clouds. We compute capsule decompositions of objects through permutation-equivariant attention, and self-supervise the process by training with pairs of randomly rotated objects. Our key idea is to aggregate the attention masks into semantic keypoints, and use these to supervise a decomposition that satisfies the capsule invariance/equivariance properties. This not only enables the training of a semantically consistent decomposition, but also allows us to learn a canonicalization operation that enables object-centric reasoning. To train our neural network we require neither classification labels nor manually-aligned training datasets. Yet, by learning an object-centric representation in a self-supervised manner, our method outperforms the state-of-the-art on 3D point cloud reconstruction, canonicalization, and unsupervised classification.

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Weiwei Sun, Andrea Tagliasacchi, Boyang Deng, Sara Sabour, Soroosh Yazdani, Geoffrey Hinton, Kwang Moo Yi. 2020-12-08. Canonical Capsules: Self-Supervised Capsules in Canonical Pose. https://arxiv.org/abs/2012.04718

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