Tree-structured Kronecker Convolutional Network for Semantic Segmentation

Understand

Most existing semantic segmentation methods employ atrous convolution to enlarge the receptive field of filters, but neglect partial information.

• To tackle this issue, we firstly propose a novel Kronecker convolution which adopts Kronecker product to expand the standard convolutional kernel for taking into account the partial feature neglected by atrous convolutions.
• Therefore, it can capture partial information and enlarge the receptive field of filters simultaneously without introducing extra parameters.
• Secondly, we propose Tree-structured Feature Aggregation (TFA) module which follows a recursive rule to expand and forms a hierarchical structure.