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Understanding the effective receptive field in semantic image segmentation

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Abstract

Deep convolutional neural networks trained with strong pixel-level supervision have recently significantly boosted the performance in semantic image segmentation. The receptive field is a crucial issue in such visual tasks, as the output must capture enough information about large objects to make a better decision. In DCNNs, the theoretical receptive field size could be very large, but the effective receptive field may be quite small. The latter is an really important factor in performance. In this work, we defined a method of measuring effective receptive field. We observed that stacking layers with large receptive field can increase the size of receptive field and increase the density of receptive field. Based on the observation, we designed a Dense Global Context Module, which makes the effective receptive field coverage larger and density higher. With the Dense Global Context Module, segmentation model reduces a large number of parameters while the performance has been substantially improved. Massive experiments proved that our Dense Global Context Module exhibits very excellent performance on the PASCAL VOC2012 and PASCAL CONTEXT data set.

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Acknowledgments

Prof. Liu is supported by the Major Projects entrusted by the National Social Science Fund of China (Under Grant 16ZH017A3) and Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) granted by Ministry of Education, China. Dr. Han is supported by the NSFC (Under Grant U1509206,61472276)

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Authors and Affiliations

  1. School of Computer and Information Engineering, Anyang Normal University, Anyang, China

    Yongge Liu

  2. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Jianzhuang Yu & Yahong Han

  3. Henan Key Laboratory of Oracle Bone Inscriptions Information Processing, Anyang Normal University, Anyang, China

    Yongge Liu

  4. Collaborative Innovation Center of International Dissemination of Chinese Language Henan Province (HNIDCL), Henan, China

    Yongge Liu

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  1. Yongge Liu
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  2. Jianzhuang Yu
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  3. Yahong Han
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Correspondence to Yahong Han.

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Liu, Y., Yu, J. & Han, Y. Understanding the effective receptive field in semantic image segmentation. Multimed Tools Appl 77, 22159–22171 (2018). https://doi.org/10.1007/s11042-018-5704-3

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  • DOI: https://doi.org/10.1007/s11042-018-5704-3

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