The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
FCN(Fully Convolutional Networks)의 도입은 핵심 객체 감지 모델에서 기록적인 발전을 이루었습니다. 그러나 입력 해상도를 유지하기 위해 언풀링이 포함된 역합성곱 네트워크가 FCN 위에 적용됩니다. 이로 인해 분할 작업에서 계산 및 네트워크 모델 크기가 증가합니다. 또한 대부분의 딥러닝 기반 방법은 항상 효과적인 것으로 보이는 효과적인 돌출성 사전 지식을 완전히 폐기합니다. 따라서 본 연구에서는 딥러닝 기반의 효율적인 Salient Object 검출 방법을 제안합니다. 이 모델에서는 확장된 컨볼루션을 네트워크에서 활용하여 풀링 및 디컨볼루션 네트워크를 추가하지 않고 고해상도의 출력을 생성합니다. 이러한 방식으로 네트워크의 매개변수와 깊이는 기존 FCN에 비해 급격히 감소합니다. 또한, 공간적 일관성과 윤곽 보존을 유지하기 위해 돌출성 개선을 위해 다양체 순위 모델을 탐색합니다. 실험 결과는 우리 방법의 성능이 다른 최신 방법보다 우수하다는 것을 확인합니다. 한편, 제안된 모델은 더 작은 모델 크기와 가장 빠른 처리 속도를 차지하므로 웨어러블 처리 시스템에 더 적합합니다.
Fei GUO
Xi'an University of Technology
Yuan YANG
Xi'an University of Technology
Yong GAO
Xi'an University of Technology
Ningmei YU
Xi'an University of Technology
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부
Fei GUO, Yuan YANG, Yong GAO, Ningmei YU, "Efficient Salient Object Detection Model with Dilated Convolutional Networks" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 10, pp. 2199-2207, October 2020, doi: 10.1587/transinf.2019EDP7284.
Abstract: Introduction of Fully Convolutional Networks (FCNs) has made record progress in salient object detection models. However, in order to retain the input resolutions, deconvolutional networks with unpooling are applied on top of FCNs. This will cause the increase of the computation and network model size in segmentation task. In addition, most deep learning based methods always discard effective saliency prior knowledge completely, which are shown effective. Therefore, an efficient salient object detection method based on deep learning is proposed in our work. In this model, dilated convolutions are exploited in the networks to produce the output with high resolution without pooling and adding deconvolutional networks. In this way, the parameters and depth of the network are decreased sharply compared with the traditional FCNs. Furthermore, manifold ranking model is explored for the saliency refinement to keep the spatial consistency and contour preserving. Experimental results verify that performance of our method is superior with other state-of-art methods. Meanwhile, the proposed model occupies the less model size and fastest processing speed, which is more suitable for the wearable processing systems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7284/_p
부
@ARTICLE{e103-d_10_2199,
author={Fei GUO, Yuan YANG, Yong GAO, Ningmei YU, },
journal={IEICE TRANSACTIONS on Information},
title={Efficient Salient Object Detection Model with Dilated Convolutional Networks},
year={2020},
volume={E103-D},
number={10},
pages={2199-2207},
abstract={Introduction of Fully Convolutional Networks (FCNs) has made record progress in salient object detection models. However, in order to retain the input resolutions, deconvolutional networks with unpooling are applied on top of FCNs. This will cause the increase of the computation and network model size in segmentation task. In addition, most deep learning based methods always discard effective saliency prior knowledge completely, which are shown effective. Therefore, an efficient salient object detection method based on deep learning is proposed in our work. In this model, dilated convolutions are exploited in the networks to produce the output with high resolution without pooling and adding deconvolutional networks. In this way, the parameters and depth of the network are decreased sharply compared with the traditional FCNs. Furthermore, manifold ranking model is explored for the saliency refinement to keep the spatial consistency and contour preserving. Experimental results verify that performance of our method is superior with other state-of-art methods. Meanwhile, the proposed model occupies the less model size and fastest processing speed, which is more suitable for the wearable processing systems.},
keywords={},
doi={10.1587/transinf.2019EDP7284},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Efficient Salient Object Detection Model with Dilated Convolutional Networks
T2 - IEICE TRANSACTIONS on Information
SP - 2199
EP - 2207
AU - Fei GUO
AU - Yuan YANG
AU - Yong GAO
AU - Ningmei YU
PY - 2020
DO - 10.1587/transinf.2019EDP7284
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2020
AB - Introduction of Fully Convolutional Networks (FCNs) has made record progress in salient object detection models. However, in order to retain the input resolutions, deconvolutional networks with unpooling are applied on top of FCNs. This will cause the increase of the computation and network model size in segmentation task. In addition, most deep learning based methods always discard effective saliency prior knowledge completely, which are shown effective. Therefore, an efficient salient object detection method based on deep learning is proposed in our work. In this model, dilated convolutions are exploited in the networks to produce the output with high resolution without pooling and adding deconvolutional networks. In this way, the parameters and depth of the network are decreased sharply compared with the traditional FCNs. Furthermore, manifold ranking model is explored for the saliency refinement to keep the spatial consistency and contour preserving. Experimental results verify that performance of our method is superior with other state-of-art methods. Meanwhile, the proposed model occupies the less model size and fastest processing speed, which is more suitable for the wearable processing systems.
ER -