2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IP-3.3
Paper Title MULTI-SCALE GRAPH CONVOLUTIONAL INTERACTION NETWORK FOR SALIENT OBJECT DETECTION
Authors Wenqi Che, Luoyi Sun, Zhifeng Xie, Youdong Ding, Kaili Han, Shanghai University, China
SessionMLR-APPL-IP-3: Machine learning for image processing 3
LocationArea F
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
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Abstract Remarkable progress has been achieved for salient object detection based on deep learning. However, most of the previous works have the issues of how to extract more effective information from scale-varying data and how to improve the boundary quality. In this paper, we propose the multi-scale graph convolutional interaction network (MGCINet), which consists of the feature interaction module (FIM), the feature aggregation module (FAM), and the residual refinement module (RRM). FIMs fuse interactive features from neighboring scales. Based on two-layers graph convolutional network, FAMs aggregate scale-specific information by graph nodes interaction. RRMs optimize the coarse saliency maps with blurred boundaries by U-net residual blocks. In addition, we propose multi-scale weighted structural loss to assign different weights to pixels while focusing on image structure at various scales. Experiments show that our method outperforms the state-of-the-arts on five benchmark datasets under different evaluation metrics.