Object-centric Learning with Cyclic Walks between Parts and Whole

• URL: http://arxiv.org/abs/2302.08023v2
• Date: Wed, 1 Nov 2023 09:08:27 GMT
• Title: Object-centric Learning with Cyclic Walks between Parts and Whole
• Authors: Ziyu Wang, Mike Zheng Shou, Mengmi Zhang
• Abstract summary: Learning object-centric representations from complex natural environments enables both humans and machines with reasoning abilities from low-level perceptual features. We propose cyclic walks between perceptual features extracted from vision transformers and object entities. In contrast to object-centric models attached with a decoder for the pixel-level or feature-level reconstructions, our cyclic walks provide strong learning signals.
• Score: 23.561434374097864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning object-centric representations from complex natural environments enables both humans and machines with reasoning abilities from low-level perceptual features. To capture compositional entities of the scene, we proposed cyclic walks between perceptual features extracted from vision transformers and object entities. First, a slot-attention module interfaces with these perceptual features and produces a finite set of slot representations. These slots can bind to any object entities in the scene via inter-slot competitions for attention. Next, we establish entity-feature correspondence with cyclic walks along high transition probability based on the pairwise similarity between perceptual features (aka "parts") and slot-binded object representations (aka "whole"). The whole is greater than its parts and the parts constitute the whole. The part-whole interactions form cycle consistencies, as supervisory signals, to train the slot-attention module. Our rigorous experiments on \textit{seven} image datasets in \textit{three} \textit{unsupervised} tasks demonstrate that the networks trained with our cyclic walks can disentangle foregrounds and backgrounds, discover objects, and segment semantic objects in complex scenes. In contrast to object-centric models attached with a decoder for the pixel-level or feature-level reconstructions, our cyclic walks provide strong learning signals, avoiding computation overheads and enhancing memory efficiency. Our source code and data are available at: \href{https://github.com/ZhangLab-DeepNeuroCogLab/Parts-Whole-Object-Centric-Learning/}{link}.

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