Please use this identifier to cite or link to this item:
|Title:||Toward achieving robust low-level and high-level scene parsing||Authors:||Shuai, Bing
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2018||Source:||Shuai, B., Ding, H., Liu, T., Wang, G., & Jiang, X. (2019). Toward achieving robust low-level and high-level scene parsing. IEEE Transactions on Image Processing, 28(3), 1378-1390. doi:10.1109/TIP.2018.2878975||Journal:||IEEE Transactions on Image Processing||Abstract:||In this paper, we address the challenging task of scene segmentation. We first discuss and compare two widely used approaches to retain detailed spatial information from pretrained CNN - "dilation" and "skip". Then, we demonstrate that the parsing performance of "skip" network can be noticeably improved by modifying the parameterization of skip layers. Furthermore, we introduce a "dense skip" architecture to retain a rich set of low-level information from pre-trained CNN, which is essential to improve the low-level parsing performance. Meanwhile, we propose a convolutional context network (CCN) and place it on top of pre-trained CNNs, which is used to aggregate contexts for high-level feature maps so that robust high-level parsing can be achieved. We name our segmentation network enhanced fully convolutional network (EFCN) based on its significantly enhanced structure over FCN. Extensive experimental studies justify each contribution separately. Without bells and whistles, EFCN achieves state-of-the-arts on segmentation datasets of ADE20K, Pascal Context, SUN-RGBD and Pascal VOC 2012.||URI:||https://hdl.handle.net/10356/142866||ISSN:||1057-7149||DOI:||10.1109/TIP.2018.2878975||Rights:||© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2018.2878975||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Journal Articles|
Files in This Item:
|Toward Achieving Robust Low-Level and High-Level Scene Parsing.pdf||2.81 MB||Adobe PDF||View/Open|
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.