Learning Dense Convolutional Embeddings for Semantic Segmentation

This paper proposes a new deep convolutional neural network (DCNN) architecture that learns pixel embeddings, such that for any two pixels on the same object, the embeddings are nearly identical. Inversely, the DCNN is trained to produce dissimilar representations for pixels coming from differing objects. Experimental results show that when this embedding network is used to augment a DCNN trained on semantic segmentation, there is a systematic improvement in per-pixel classification accuracy. This strategy is complementary to many others pursued in semantic segmentation, and it is implemented efficiently in a popular deep learning framework, making its integration with existing systems very straightforward.