Deep recursive segmentation networks심층 제귀 영역 분할 신경망

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This paper considers building recursive layers that are unrolled to increase depth of the architecture in the task of semantic segmentation. Currently, results from the state-of-the-art models illustrate that increasing the physical depth, that is adding more layers with skip connections, effectively boosts the segmentation performance. However, there are two main issues for the very deep models. Firstly, deeper models requires more labeled data to train, which is labor expensive for computer vision tasks, such as detection and segmentation. Secondly, most of very deep models are feed forward, which fails to support rapid visual recognition. In this work, Deep Recursive Segmentation Networks (DRSN) are proposed that reuse a portion of parameters during feed forward process. DRSN have two exceptional advantages over previous models. Firstly, DRSN contains a limited amount of parameters meanwhile making deeper model depth. Secondly, DRSN support rapid visual recognition that is vital in some applications, such as robots and autonomous cars. While utilizing only 15% of parameters of previous FCN models, we achieve 80% the performance on the pixel accuracy.
Advisors
Yoo, Chang Dongresearcher유창동researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2018
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2018.2,[iii, 23 p. :]

Keywords

Semantic segmentation▼arecurrent neural networks▼arecursive neural networks▼aconvolutional neural networks▼afully convolutional networks

URI
http://hdl.handle.net/10203/266855
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=733976&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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