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：本文研究了一种新颖的推理范式，称为模式推理，它通过学习在主流哲学认知概念“模式”的引导下，通过重建先验深度神经网络的前向传播机制，演绎性地推导出可解释的预测结果。我们致力于将传统模型推理流程重新表述为一种图匹配策略，该策略将图像中提取的视觉概念与预先计算的场景印象相关联，类比于人类通过印象匹配进行推理的机制。为此，我们设计了一种精密的架构，命名为SchemaNet，作为所提出的模式推理概念的专用实例化实现。该架构将输入实例的视觉语义以及目标类别的习得抽象想象均建模为拓扑关系图。同时，为了从全局视角捕捉并利用视觉语义的组成性贡献，我们还在SchemaNet中引入了一种通用的Feat2Graph方案，以建立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，所提出的模式推理实现了令人鼓舞的性能，并同时清晰展示了导向预测的演绎过程。我们的代码可在https://github.com/zhfeing/SchemaNet-PyTorch获取。