In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the prevalent philosophical cognitive concept of schema. We strive to reformulate the conventional model inference pipeline into a graph matching policy that associates the extracted visual concepts of an image with the pre-computed scene impression, by analogy with human reasoning mechanism via impression matching. To this end, we devise an elaborated architecture, termed as SchemaNet, as a dedicated instantiation of the proposed schema inference concept, that models both the visual semantics of input instances and the learned abstract imaginations of target categories as topological relational graphs. Meanwhile, to capture and leverage the compositional contributions of visual semantics in a global view, we also introduce a universal Feat2Graph scheme in SchemaNet to establish the relational graphs that contain abundant interaction information. Both the theoretical analysis and the experimental results on several benchmarks demonstrate that the proposed schema inference achieves encouraging performance and meanwhile yields a clear picture of the deductive process leading to the predictions. Our code is available at https://github.com/zhfeing/SchemaNet-PyTorch.

翻译：本文研究一种新颖的推理范式——图式推理，通过借鉴哲学认知概念中的“图式”理论，重构深度神经网络前向传播机制，以演绎方式学习可解释的预测过程。我们将传统模型推理流程重构为图匹配策略，通过类比人类基于印象匹配的推理机制，将图像中提取的视觉概念与预计算场景印象相关联。为此，我们设计了名为SchemaNet的精巧架构作为所提出图式推理概念的具体实例化，该架构同时将输入实例的视觉语义和目标类别的学习抽象想象建模为拓扑关系图。同时，为全局性地捕获并利用视觉语义的组合贡献，我们在SchemaNet中引入通用Feat2Graph方案，构建包含丰富交互信息的关系图。理论分析与多个基准数据集上的实验结果表明，本文提出的图式推理方法不仅取得了令人鼓舞的性能，还能清晰呈现导致预测结果的演绎过程。我们的代码已开源：https://github.com/zhfeing/SchemaNet-PyTorch。