Inspired by biological mechanisms and structures in neuroscience, many biologically inspired visual computational models have been presented to provide new solutions for visual recognition task. For example, convolutional neural network (CNN) was proposed according to the hierarchical structure of biological vision, which could achieve superior performance in large-scale image classification. In this paper, we propose a new framework called visual interaction networks (VIN-Net), which is inspired by visual interaction mechanisms. More specifically, self-interaction, mutual-interaction, multi-interaction, and adaptive interaction are proposed in VIN-Net, forming the first interactive completeness of the visual interaction model. To further enhance the representation ability of visual features, the adaptive adjustment mechanism is integrated into the VIN-Net model. Finally, our model is evaluated on three benchmark datasets and two self-built textile defect datasets. The experimental results demonstrate that the proposed model exhibits its efficiency on visual classification tasks. Furthermore, a textile industrial application shows that the proposed architecture outperforms the state-of-the-art approaches in classification performance.

受神经科学的生物学机制和结构的启发，提出了许多生物学启发的视觉计算模型，为视觉识别任务提供了新的解决方案。例如，根据生物视觉的层次结构提出了卷积神经网络（CNN），可以在大规模图像分类中取得优异的性能。在本文中，我们提出了一个称为视觉交互网络（VIN-Net）的新框架，该框架受视觉交互机制的启发。更具体地说，在VIN-Net中提出了自交互，互交互，多交互和自适应交互，从而形成了视觉交互模型的第一个交互完整性。为了进一步增强视觉特征的表现能力，自适应调整机制已集成到VIN-Net模型中。最后，我们的模型在三个基准数据集和两个自建的纺织品缺陷数据集上进行了评估。实验结果表明，提出的模型在视觉分类任务上表现出效率。此外，在纺织工业中的应用表明，提出的体系结构在分类性能方面优于最新方法。