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arXiv · 1509.00552

DAG-Recurrent Neural Networks For Scene Labeling

Bing Shuai·Zhen Zuo·Gang Wang·Bing Wang
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Abstract

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.

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Bing Shuai, Zhen Zuo, Gang Wang, Bing Wang. 2015-11-23. DAG-Recurrent Neural Networks For Scene Labeling. https://arxiv.org/abs/1509.00552

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