Global Context-Aware Progressive Aggregation Network for Salient Object Detection

By Zuyao Chen, Qianqian Xu, Runmin Cong, Qingming Huang. AAAI Conference on Artificial Intelligence, 2020

492 citations recorded by the source corpus.

Deep convolutional neural networks have achieved competitive performance in salient object detection, in which how to learn effective and comprehensive features plays a critical role. Most of the previous works mainly adopted multiple-level feature integration yet ignored the gap between different features. Besides, there also exists a dilution process of high-level features as they passed on the top-down pathway. To remedy these issues, we propose a novel network named GCPANet to effectively integrate low-level appearance features, high-level semantic features, and global context features through some progressive context-aware Feature Interweaved Aggregation (FIA) modules and generate the saliency map in a supervised way. Moreover, a Head Attention (HA) module is used to reduce information redundancy and enhance the top layers features by leveraging the spatial and channel-wise attention, and the Self Refinement (SR) module is utilized to further refine and heighten the input features. Furthermore, we design the Global Context Flow (GCF) module to generate the global context information at different stages, which aims to learn the relationship among different salient regions and alleviate the dilution effect of high-level features. Experimental results on six benchmark datasets demonstrate that the proposed approach outperforms the state-of-the-art methods both quantitatively and qualitatively.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/77f486a26f7b92eb975dcf0d94cb6b7987765a47

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Ling Shao
2. Deng-Ping Fan
3. Ming-Ming Cheng
4. Ge-Peng Ji
5. Ali Borji
6. Dingwen Zhang
7. Nian Liu
8. Yun Liu

In Computer Vision and Pattern Recognition

1. Deng-Ping Fan
2. Ling Shao
3. Ming-Ming Cheng
4. Jianbing Shen
5. Ge-Peng Ji
6. Huchuan Lu
7. Ali Borji
8. Wenguan Wang

Citations in the corpus

• Camouflaged Object Segmentation with Distraction Mining (2021, methodology): “…detection methods DSC [20] and BDRAR [72]; medical image segmentation methods UNet++ [71] and PraNet [13]; salient object detection methods PiCANet [33], BASNet [44], CPD [55], PFANet [69], EGNet [67], F3Net [53], GC-PANet [5], and MINet-R [40]; and camouflaged object segmentation method SINet [12].”
• Structure-Measure: A New Way to Evaluate Foreground Maps (2017, background): “, RGB SOD (Zhuge et al., 2021a; Zhang et al., 2020a; Chen et al., 2020; Zhao et al., 2019), RGB-D SOD (Zhang et al.”
• Salient Object Detection via Integrity Learning (2021, methodology): “GCPANet [60] made full use of global context to capture the relationships between multiple salient objects”