  Semantic image segmentation is one of the most challenged tasks in computer
vision. In this paper, we propose a highly fused convolutional network, which
consists of three parts: feature downsampling, combined feature upsampling and
multiple predictions. We adopt a strategy of multiple steps of upsampling and
combined feature maps in pooling layers with its corresponding unpooling
layers. Then we bring out multiple pre-outputs, each pre-output is generated
from an unpooling layer by one-step upsampling. Finally, we concatenate these
pre-outputs to get the final output. As a result, our proposed network makes
highly use of the feature information by fusing and reusing feature maps. In
addition, when training our model, we add multiple soft cost functions on
pre-outputs and final outputs. In this way, we can reduce the loss reduction
when the loss is back propagated. We evaluate our model on three major
segmentation datasets: CamVid, PASCAL VOC and ADE20K. We achieve a
state-of-the-art performance on CamVid dataset, as well as considerable
improvements on PASCAL VOC dataset and ADE20K dataset
