【摘要】 针对卷积神经网络的庞大参数量和计算量难以应用于移动设备或嵌入式设备的问题,提出冗余特征重建模块(redundant feature reconstruction,RFR)和组注意力卷积模块(group attention convolution,GAC),RFR模块使用较少的参数量提取重要的固有特征,通过线性算子重建冗余特征帮助网络模型推理,GAC模块通过引入注意力机制改善分组卷积中各分组之间的信息交互不充分的问题,基于所提出模块构建一种高效轻量化的语义分割网络。在Cityscapes数据集上进行的实验显示了所提网络模型达到了良好的分割准确率,参数量和网络模型大小低于当前典型语义分割算法,在算法复杂度和性能上达到了平衡,表明了其有效性。更多还原

【Abstract】 Aiming at the problem that the huge amount of parameters and calculations of deep convolutional neural networks are difficult to apply to mobile devices or embedded devices,the redundant feature reconstruction(RFR)module and group attention convolution(GAC)module were proposed.The RFR module used a small amount of parameters to extract important inherent features,and used linear operators to reconstruct redundant features to help network model reasoning.The GAC module improved the problem of insufficient information interaction between groups of group convolution by introducing attention mechanism,and an efficient and lightweight semantic segmentation network was constructed based on the proposed module.Experiments on Cityscapes dataset show that the proposed network model achieves good accuracy,while the parameter amount and network model size are lower than current typical semantic segmentation algorithms,achieving a balance between algorithm complexity and performance,indicating the effectiveness of the proposed module.更多还原