Scene Labeling using Recurrent Neural Networks with Explicit Long Range Contextual Dependency

Spatial contextual dependencies are crucial for scene labeling problems. Recurrent neural network (RNN) is one of state-of-the-art methods for modeling contextual dependencies. However, RNNs are fundamentally designed for sequential data, not spatial data. This work shows that directly applying traditional RNN architectures, which unfold a 2D lattice grid into a sequence, is not sufficient to model structure dependencies in images due to the "impact vanishing" problem. A new RNN unit with Explicit Long-range Conditioning (RNN-ELC) is designed to overcome this problem. Based on this new RNN-ELC unit, a novel neural network architecture is built for scene labeling tasks. This architecture achieves state-of-the-art performances on several standard scene labeling datasets. Comprehensive experiments demonstrate that scene labeling tasks benefit a lot from the explicit long range contextual dependencies encoded in our algorithm.