Related papers: Rotating Features for Object Discovery

Rotating Features for Object Discovery

URL: http://arxiv.org/abs/2306.00600v2
Date: Tue, 17 Oct 2023 12:48:58 GMT
Title: Rotating Features for Object Discovery
Authors: Sindy L\"owe, Phillip Lippe, Francesco Locatello, Max Welling
Abstract summary: We present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations. Together, these advancements enable us to scale distributed object-centric representations from simple toy to real-world data.
Score: 74.1465486264609
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: The binding problem in human cognition, concerning how the brain represents and connects objects within a fixed network of neural connections, remains a subject of intense debate. Most machine learning efforts addressing this issue in an unsupervised setting have focused on slot-based methods, which may be limiting due to their discrete nature and difficulty to express uncertainty. Recently, the Complex AutoEncoder was proposed as an alternative that learns continuous and distributed object-centric representations. However, it is only applicable to simple toy data. In this paper, we present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations. Additionally, we show the applicability of our approach to pre-trained features. Together, these advancements enable us to scale distributed object-centric representations from simple toy to real-world data. We believe this work advances a new paradigm for addressing the binding problem in machine learning and has the potential to inspire further innovation in the field.

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