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
돌출 영역 감지는 컴퓨터 비전 및 이미지 처리의 근본적인 문제입니다. 딥 러닝 모델은 기존 접근 방식보다 성능이 뛰어나지만 매개변수가 크고 속도가 느리다는 단점이 있습니다. 이러한 문제를 해결하기 위해 본 논문에서는 CNN(Convolution Neural Network) 기반의 효율적인 돌출 영역 탐지 아키텍처인 다중 기능 융합 네트워크(MFFN)를 제안합니다. 새로운 특징 추출 구조는 CNN에서 특징 맵을 얻기 위해 설계되었습니다. 융합 밀집 블록은 모든 하위 수준 및 상위 수준 특징 맵을 융합하여 두드러진 영역 결과를 도출하는 데 사용됩니다. MFFN은 사후 처리 절차가 필요하지 않은 엔드 투 엔드 아키텍처입니다. 벤치마크 데이터세트에 대한 실험에서는 MFFN이 돌출 영역 감지에 대한 최첨단 성능을 달성하고 훨씬 더 적은 매개변수와 계산 시간이 필요하다는 것을 보여줍니다. 절제 실험은 MFFN의 각 모듈의 효율성을 보여줍니다.
Zheng FANG
Army Engineering University
Tieyong CAO
Army Engineering University
Jibin YANG
Army Engineering University
Meng SUN
Army Engineering University
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부
Zheng FANG, Tieyong CAO, Jibin YANG, Meng SUN, "Multi-Feature Fusion Network for Salient Region Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 6, pp. 834-841, June 2019, doi: 10.1587/transfun.E102.A.834.
Abstract: Salient region detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the multi-feature fusion network (MFFN) - a efficient salient region detection architecture based on Convolution Neural Network (CNN). A novel feature extraction structure is designed to obtain feature maps from CNN. A fusion dense block is used to fuse all low-level and high-level feature maps to derive salient region results. MFFN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that MFFN achieves the state-of-the-art performance on salient region detection and requires much less parameters and computation time. Ablation experiments demonstrate the effectiveness of each module in MFFN.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.834/_p
부
@ARTICLE{e102-a_6_834,
author={Zheng FANG, Tieyong CAO, Jibin YANG, Meng SUN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Multi-Feature Fusion Network for Salient Region Detection},
year={2019},
volume={E102-A},
number={6},
pages={834-841},
abstract={Salient region detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the multi-feature fusion network (MFFN) - a efficient salient region detection architecture based on Convolution Neural Network (CNN). A novel feature extraction structure is designed to obtain feature maps from CNN. A fusion dense block is used to fuse all low-level and high-level feature maps to derive salient region results. MFFN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that MFFN achieves the state-of-the-art performance on salient region detection and requires much less parameters and computation time. Ablation experiments demonstrate the effectiveness of each module in MFFN.},
keywords={},
doi={10.1587/transfun.E102.A.834},
ISSN={1745-1337},
month={June},}
부
TY - JOUR
TI - Multi-Feature Fusion Network for Salient Region Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 834
EP - 841
AU - Zheng FANG
AU - Tieyong CAO
AU - Jibin YANG
AU - Meng SUN
PY - 2019
DO - 10.1587/transfun.E102.A.834
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2019
AB - Salient region detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the multi-feature fusion network (MFFN) - a efficient salient region detection architecture based on Convolution Neural Network (CNN). A novel feature extraction structure is designed to obtain feature maps from CNN. A fusion dense block is used to fuse all low-level and high-level feature maps to derive salient region results. MFFN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that MFFN achieves the state-of-the-art performance on salient region detection and requires much less parameters and computation time. Ablation experiments demonstrate the effectiveness of each module in MFFN.
ER -