Towards Self-Supervised Learning of Global and Object-Centric
Representations
- URL: http://arxiv.org/abs/2203.05997v1
- Date: Fri, 11 Mar 2022 15:18:47 GMT
- Title: Towards Self-Supervised Learning of Global and Object-Centric
Representations
- Authors: Federico Baldassarre, Hossein Azizpour
- Abstract summary: We discuss key aspects of learning structured object-centric representations with self-supervision.
We validate our insights through several experiments on the CLEVR dataset.
- Score: 4.36572039512405
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Self-supervision allows learning meaningful representations of natural images
which usually contain one central object. How well does it transfer to
multi-entity scenes? We discuss key aspects of learning structured
object-centric representations with self-supervision and validate our insights
through several experiments on the CLEVR dataset. Regarding the architecture,
we confirm the importance of competition for attention-based object discovery,
where each image patch is exclusively attended by one object. For training, we
show that contrastive losses equipped with matching can be applied directly in
a latent space, avoiding pixel-based reconstruction. However, such an
optimization objective is sensitive to false negatives (recurring objects) and
false positives (matching errors). Thus, careful consideration is required
around data augmentation and negative sample selection.
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