GRANet: Global refinement atrous convolutional neural network for semantic scene segmentation

The main problems of complex-scene understanding and semantic scene segmentation are caused by mismatched relationships, confusion categories, and inconspicuous classes. Towards above issues, we propose a global refinement atrous convolutional neural network (GRANet) for semantic scene segmentation. To enlarge the receptive field of filters, we use atrous convolution instead of the downsampling operators. To handle the challenge caused by the existence of objects at multiple scales in a scene, we adopt multiple rates atrous convolution structure. And to overcome the problem that the current semantic segmentation architecture can not make good use of global information, we propose a multiple pooling module schemes to utilize the global context information to boost the performance of our GRANet. The proposed GRANet achieves state-of-the-art performance on the SiftFlow Dataset and attains comparable performance with other state-of-the-art works on Cityscapes dataset.