Edge-guided Hierarchically Nested Network for Real-time Semantic Segmentation

Yuqi Li, Sei Ichiro Kamata, Haoran Liu

研究成果: Conference contribution

抄録

Semantic segmentation requires both large region context information and rich spatial information. The former can be achieved by a deep-enough network structure. However, most spatial information lost in repeated down-sample operation in deep Convolutional Neural Networks cannot be easily recovered. In this paper, we propose a novel edge-guided hierarchically nested network to improve both speed and accuracy of real-time semantic segmentation. We introduce Edge Guidance Unit which aims to guide the decoder network to exploit high-resolution clues, recover and refine spatial information with low additional computational cost. We also introduce a well-designed Fusion Block to combine different resolution features along with edge guidance. In experiments we demonstrate that our network stabilizes the small object region while achieving a result of 72.7% mean IoU at 40 FPS on CityScapes test dataset.

本文言語English
ホスト出版物のタイトルProceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ296-301
ページ数6
ISBN(電子版)9781728133775
DOI
出版ステータスPublished - 2019 9月
イベント2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019 - Kuala Lumpur, Malaysia
継続期間: 2019 9月 172019 9月 19

出版物シリーズ

名前Proceedings of the 2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019

Conference

Conference2019 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2019
国/地域Malaysia
CityKuala Lumpur
Period19/9/1719/9/19

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • コンピュータ ビジョンおよびパターン認識
  • 信号処理
  • 健康情報学
  • 人工知能

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