  While most existing segmentation methods usually combined the powerful
feature extraction capabilities of CNNs with Conditional Random Fields (CRFs)
post-processing, the result always limited by the fault of CRFs . Due to the
notoriously slow calculation speeds and poor efficiency of CRFs, in recent
years, CRFs post-processing has been gradually eliminated. In this paper, an
improved Generative Adversarial Networks (GANs) for image semantic segmentation
task (semantic segmentation by GANs, Seg-GAN) is proposed to facilitate further
segmentation research. In addition, we introduce Convolutional CRFs (ConvCRFs)
as an effective improvement solution for the image semantic segmentation task.
Towards the goal of differentiating the segmentation results from the ground
truth distribution and improving the details of the output images, the proposed
discriminator network is specially designed in a full convolutional manner
combined with cascaded ConvCRFs. Besides, the adversarial loss aggressively
encourages the output image to be close to the distribution of the ground
truth. Our method not only learns an end-to-end mapping from input image to
corresponding output image, but also learns a loss function to train this
mapping. The experiments show that our method achieves better performance than
state-of-the-art methods.
