  In image labeling, local representations for image units are usually
generated from their surrounding image patches, thus long-range contextual
information is not effectively encoded. In this paper, we introduce recurrent
neural networks (RNNs) to address this issue. Specifically, directed acyclic
graph RNNs (DAG-RNNs) are proposed to process DAG-structured images, which
enables the network to model long-range semantic dependencies among image
units. Our DAG-RNNs are capable of tremendously enhancing the discriminative
power of local representations, which significantly benefits the local
classification. Meanwhile, we propose a novel class weighting function that
attends to rare classes, which phenomenally boosts the recognition accuracy for
non-frequent classes. Integrating with convolution and deconvolution layers,
our DAG-RNNs achieve new state-of-the-art results on the challenging SiftFlow,
CamVid and Barcelona benchmarks.
