TIIS (한국인터넷정보학회)
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| 한글제목(Korean Title) |
AANet: Adjacency auxiliary network for salient object detection |
| 영문제목(English Title) |
AANet: Adjacency auxiliary network for salient object detection |
| 저자(Author) |
Xialu Li
Ziguan Cui
Zongliang Gan
Guijin Tang
Feng Liu
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| 원문수록처(Citation) |
VOL 15 NO. 10 PP. 3729 ~ 3749 (2021. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
At present, deep convolution network-based salient object detection (SOD) has achieved impressive performance. However, it is still a challenging problem to make full use of the multi-scale information of the extracted features and which appropriate feature fusion method is adopted to process feature mapping. In this paper, we propose a new adjacency auxiliary network (AANet) based on multi-scale feature fusion for SOD. Firstly, we design the parallel connection feature enhancement module (PFEM) for each layer of feature extraction, which improves the feature density by connecting different dilated convolution branches in parallel, and add channel attention flow to fully extract the context information of features. Then the adjacent layer features with close degree of abstraction but different characteristic properties are fused through the adjacent auxiliary module (AAM) to eliminate the ambiguity and noise of the features. Besides, in order to refine the features effectively to get more accurate object boundaries, we design adjacency decoder (AAM_D) based on adjacency auxiliary module (AAM), which concatenates the features of adjacent layers, extracts their spatial attention, and then combines them with the output of AAM. The outputs of AAM_D features with semantic information and spatial detail obtained from each feature are used as salient prediction maps for multi-level feature joint supervising. Experiment results on six benchmark SOD datasets demonstrate that the proposed method outperforms similar previous methods. |
| 키워드(Keyword) |
Salient Object Detection
Deep Learning
Convolutional Neural Network
Multi-scale Information
Multi-level Feature Fusion
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| 파일첨부 |
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