Improving the discrimination between foreground and background for semantic segmentation

One challenging problem in semantic segmentation is due to the erroneous predictions between categorical foreground and cluttered background. To address it, we propose to utilize a fused loss function to train a fully convolutional network, which aims to enhance the discrimination between foreground and background in images. In addition, we propose a pixel objectness (POS) to measure the importance of pixels. POS is able to recover some missing foreground pixels from the background. Experimental results on the PASCAL VOC 2012 dataset demonstrate our approach can achieve considerable improvements compared with the baseline counterpart, while maintaining the ease of training deep networks.