Although Convolutional Neural Networks are effective visual models that generate hierarchies of features, there still exist some shortcomings in the application of Deep Convolutional Neural Networks to semantic image segmentation. In this work, our algorithm incorporates multi-scale atrous convolution, attention model and Conditional Random Fields to tackle this problem. Firstly, our method replaces deconvolutional layers with atrous convolutional layers to avoid reducing feature resolution when the Deep Convolutional Neural Networks is employed in a fully convolutional fashion. Secondly, mult...
