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：在本文中,我们研究一种被称为 " 化学推断 " 的新颖的推论范式,它学会通过重建先前深神经网络(DNN)预发计划,在流行的哲学认知系统概念的指导下,通过重建先前深神经网络(DNN)预发计划,推断出可以解释的预测。我们努力将传统模型推导管道改成一个图表匹配政策,将图像的提取视觉概念与预审的场景印象挂钩,通过图像匹配与人类推理机制进行类比。为此,我们设计了一个精心设计的架构,称为 SchemaNet,作为拟议的系统推断概念的专用即时化概念,该架构既模拟了投入实例的视觉语义,又模拟了作为表层关系图的各类目标的抽象想象力。同时,为了在全球观点中捕捉和利用视觉语义学的构成贡献,我们还在SchemaNet引入了一个通用的Feat2Graph 计划,以建立包含大量互动信息的关联图。两个理论分析和若干基准的实验结果都表明,拟议的系统图理学分析能够鼓励我们进行预测。</s>