Emcenet: Efficient Multi-Scale Context Exploration Network for Salient Object Detection
Yanguang Sun, Chenxing Xia, Xiuju Gao, Bin Ge, Hanling Zhang, Kuan‐Ching Li · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
Multi-scale context is crucial for the accurate salient object detection (SOD) in the real-world scenes. Although current contextual information-based SOD methods have achieved great progress, they may fail to generate precise saliency maps due to their seldom considering the correlation of different scale context during the extraction process. To address these issues, we propose an Efficient Multi-Scale Context Exploration Network (EMCENet) for SOD. Specifically, a progressive multi-scale context extraction (PMCE) module is designed to progressively capture strongly correlated multi-scale context by using multi-receptive-field convolution operations. Afterwards, a hierarchical feature hybrid interaction (HFHI) module is introduced to generate powerful feature representations by adaptively aggregating multi-level features in a hybrid interaction strategy. Extensive experimental results on six public datasets demonstrate that the proposed EMCENet method without any post-processing performs favorably against 13 state-of-the-art SOD methods.