Object-Centric Case-Based Reasoning via Argumentation

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Gabriel de Olim Gaul, Adam Gould, Avinash Kori, Francesca Toni

Published: 2025, Last Modified: 23 Mar 2026CoRR 2025EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: We introduce Slot Attention Argumentation for Case-Based Reasoning (SAA-CBR), a novel neuro-symbolic pipeline for image classification that integrates object-centric learning via a neural Slot Attention (SA) component with symbolic reasoning conducted by Abstract Argumentation for Case-Based Reasoning (AA-CBR). We explore novel integrations of AA-CBR with the neural component, including feature combination strategies, casebase reduction via representative samples, novel count-based partial orders, a One-Vs-Rest strategy for extending AA-CBR to multi-class classification, and an application of Supported AA-CBR, a bipolar variant of AA-CBR. We demonstrate that SAA-CBR is an effective classifier on the CLEVR-Hans datasets, showing competitive performance against baseline models.