Visual abstract reasoning tasks present challenges for deep neural networks, exposing limitations in their capabilities. In this work, we present a neural network model that addresses the challenges posed by Raven's Progressive Matrices (RPM). Inspired by the two-stream hypothesis of visual processing, we introduce the Dual-stream Reasoning Network (DRNet), which utilizes two parallel branches to capture image features. On top of the two streams, a reasoning module first learns to merge the high-level features of the same image. Then, it employs a rule extractor to handle combinations involving the eight context images and each candidate image, extracting discrete abstract rules and utilizing an multilayer perceptron (MLP) to make predictions. Empirical results demonstrate that the proposed DRNet achieves state-of-the-art average performance across multiple RPM benchmarks. Furthermore, DRNet demonstrates robust generalization capabilities, even extending to various out-of-distribution scenarios. The dual streams within DRNet serve distinct functions by addressing local or spatial information. They are then integrated into the reasoning module, leveraging abstract rules to facilitate the execution of visual reasoning tasks. These findings indicate that the dual-stream architecture could play a crucial role in visual abstract reasoning.

翻译：视觉抽象推理任务对深度神经网络提出了挑战，揭示了其能力上的局限性。本研究提出了一种神经网络模型，旨在解决瑞文渐进矩阵所提出的挑战。受视觉处理双流假说的启发，我们引入了双流推理网络，该网络利用两个并行分支来捕获图像特征。在两个流之上，一个推理模块首先学习合并同一图像的高级特征。随后，它采用一个规则提取器来处理涉及八个上下文图像与每个候选图像的组合，提取离散的抽象规则，并利用一个多层感知机进行预测。实证结果表明，所提出的双流推理网络在多个瑞文渐进矩阵基准测试中达到了最先进的平均性能。此外，双流推理网络展现出强大的泛化能力，甚至能扩展到各种分布外场景。双流推理网络中的双流通过处理局部或空间信息来发挥不同的功能。它们随后被整合到推理模块中，利用抽象规则来促进视觉推理任务的执行。这些发现表明，双流架构可能在视觉抽象推理中扮演关键角色。