Abstract
Higher levels of machine intelligence demand alignment with human perception and cognition. Deep neural networks (DNN) dominated machine intelligence have demonstrated exceptional performance across various real-world tasks. Nevertheless, recent evidence suggests that DNNs fail to perceive illusory contours like the abutting grating, a discrepancy that misaligns with human perception patterns. Departing from previous works, we propose a novel deep network called illusory contour perception network (ICPNet) inspired by the circuits of the visual cortex. In ICPNet, a multi-scale feature projection (MFP) module is designed to extract multi-scale representations. To boost the interaction between feedforward and feedback features, a feature interaction attention module (FIAM) is introduced. Moreover, drawing inspiration from the shape bias observed in human perception, an edge detection task conducted via the edge fusion module (EFM) injects shape constraints that guide the network to concentrate on the foreground. We assess our method on the existing AG-MNIST test set and the AG-Fashion-MNIST test sets constructed by this work. Comprehensive experimental results reveal that ICPNet is significantly more sensitive to abutting grating illusory contours than state-of-the-art models, with notable improvements in top-1 accuracy across various subsets. This work is expected to make a step towards human-level intelligence for DNN-based models.
Abstract (translated)
更高的机器智能需要与人类感知和认知相匹配。深度神经网络（DNN）主导的机器智能在各种现实世界任务中表现出卓越性能。然而，最近的研究表明，DNN无法识别像相邻条栅这样的错觉轮廓，这种差异与人类感知模式不符。不同于以往的工作，我们提出了一种新的深度网络——错觉轮廓感知网络（ICPNet），它受到了视觉皮层回路的启发。在ICPNet中，设计了一个多尺度特征投影（MFP）模块来提取多尺度表示。为了增强前馈和反馈特征之间的交互作用，引入了特征互动注意模块（FIAM）。此外，借鉴人类感知中的形状偏好的观察结果，我们通过边缘融合模块（EFM）执行的边缘检测任务注入了形状约束，引导网络专注于前景。 我们在现有的AG-MNIST测试集以及本研究构建的AG-Fashion-MNIST测试集上评估了我们的方法。全面的实验结果显示，ICPNet比现有最先进的模型对相邻条栅错觉轮廓更敏感，在各种子集上的顶级准确率都有显著提高。这项工作有望使基于DNN的模型向人类级别的智能迈出一步。
URL
https://arxiv.org/abs/2508.17254
