用对象中心注意力与符号推理结合，提升图像分类准确性
Object-Centric Case-Based Reasoning via Argumentation
- 通过槽注意力提取图像对象特征，结合符号推理进行决策
- 在CLEVR-Hans数据集上表现媲美基线模型，支持多类别分类
- 适合需要可解释性与推理能力的视觉任务研究者
我们提出一种新型神经符号框架SAA-CBR，将基于槽注意力（Slot Attention）的对象中心学习与抽象论证式案例推理（AA-CBR）相结合。探索了特征融合策略、代表性样本减少案例库、基于数量的偏序关系、一对一多分类扩展方法，以及支持型AA-CBR（一种双极变体）的应用。实验表明，SAA-CBR在CLEVR-Hans数据集上表现优异，性能可与基线模型媲美。
原文摘要 · Abstract (English)
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.
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