  Despite the growing success of Convolution neural networks (CNN) in the
recent past in the task of scene segmentation, the standard models lack some of
the important features that might result in sub-optimal segmentation outputs.
The widely used encoder-decoder architecture extracts and uses several
redundant and low-level features at different steps and different scales. Also,
these networks fail to map the long-range dependencies of local features, which
results in discriminative feature maps corresponding to each semantic class in
the resulting segmented image. In this paper, we propose a novel multi-scale
attention network for scene segmentation purposes by using the rich contextual
information from an image. Different from the original UNet architecture we
have used attention gates which take the features from the encoder and the
output of the pyramid pool as input and produced out-put is further
concatenated with the up-sampled output of the previous pyramid-pool layer and
mapped to the next subsequent layer. This network can map local features with
their global counterparts with improved accuracy and emphasize on
discriminative image regions by focusing on relevant local features only. We
also propose a compound loss function by optimizing the IoU loss and fusing
Dice Loss and Weighted Cross-entropy loss with it to achieve an optimal
solution at a faster convergence rate. We have evaluated our model on two
standard datasets named PascalVOC2012 and ADE20k and was able to achieve mean
IoU of 79.88% and 44.88% on the two datasets respectively, and compared our
result with the widely known models to prove the superiority of our model over
them.
