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Semantic image segmentation is one of the most challenged tasks in computervision. In this paper, we propose a highly fused convolutional network, whichconsists of three parts: feature downsampling, combined feature upsampling andmultiple predictions. We adopt a strategy of multiple steps of upsampling andcombined feature maps in pooling layers with its corresponding unpoolinglayers. Then we bring out multiple pre-outputs, each pre-output is generatedfrom an unpooling layer by one-step upsampling. Finally, we concatenate thesepre-outputs to get the final output. As a result, our proposed network makeshighly use of the feature information by fusing and reusing feature maps. Inaddition, when training our model, we add multiple soft cost functions onpre-outputs and final outputs. In this way, we can reduce the loss reductionwhen the loss is back propagated. We evaluate our model on three majorsegmentation datasets: CamVid, PASCAL VOC and ADE20K. We achieve astate-of-the-art performance on CamVid dataset, as well as considerableimprovements on PASCAL VOC dataset and ADE20K dataset

Semantic Segmentation via Highly Fused Convolutional Network with Multiple Soft Cost Functions