Object-Centric Case-Based Reasoning via Argumentation
This work addresses image classification for AI systems by proposing a neuro-symbolic approach, though it appears incremental as it builds on existing methods like Slot Attention and Argumentation.
The paper tackled image classification by integrating object-centric neural learning with symbolic reasoning, resulting in a novel pipeline that demonstrated competitive performance on CLEVR-Hans datasets.
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.