Learning Dense Convolutional Embeddings for Semantic Segmentation
Abstract
This paper proposes a new deep convolutional neural network (DCNN) architecture
that learns pixel embeddings, such that pairwise distances between the embeddings
can be used to infer whether or not the pixels lie on the same region.
That is, for any two pixels on the same object, the embeddings are trained to be
similar; for any pair that straddles an object boundary, the embeddings are trained
to be dissimilar. Experimental results show that when this embedding network
is used in conjunction with a DCNN trained on semantic segmentation, there is a
systematic improvement in per-pixel classification accuracy. Our contributions are
integrated in the popular Caffe deep learning framework, and consist in straightforward
modifications to convolution routines. As such, they can be exploited for
any task involving convolution layers.