A biological vision inspired framework for machine perception of abutting grating illusory contours
Xiao Zhang, Kai-Fu Yang, Xian-Shi Zhang, Hong-Zhi You, Hong-Mei Yan, Yong-Jie Li

TL;DR
This paper introduces ICPNet, a biologically inspired deep neural network designed to better perceive illusory contours like abutting gratings, aligning machine perception more closely with human visual cognition.
Contribution
The paper presents a novel deep network architecture with modules inspired by visual cortex circuits, improving DNN sensitivity to illusory contours compared to existing models.
Findings
ICPNet outperforms state-of-the-art models in detecting abutting grating illusions.
Significant improvements in top-1 accuracy on AG-MNIST and AG-Fashion-MNIST datasets.
The model demonstrates enhanced alignment with human perception of illusory contours.
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…
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