Papers › Stacked Cross Refinement Network for Edge-Aware Salient Object Detection

Stacked Cross Refinement Network for Edge-Aware Salient Object Detection

1 Oct 2019ICCV 2019 10archive 2025-07-28

Zhe Wu, Li Su, Qingming Huang

Salient object detection is a fundamental computer vision task. The majority of existing algorithms focus on aggregating multi-level features of pre-trained convolutional neural networks. Moreover, some researchers attempt to utilize edge information for auxiliary training. However, existing edge-aware models design unidirectional frameworks which only use edge features to improve the segmentation features. Motivated by the logical interrelations between binary segmentation and edge maps, we propose a novel Stacked Cross Refinement Network (SCRN) for salient object detection in this paper. Our framework aims to simultaneously refine multi-level features of salient object detection and edge detection by stacking Cross Refinement Unit (CRU). According to the logical interrelations, the CRU designs two direction-specific integration operations, and bidirectionally passes messages between the two tasks. Incorporating the refined edge-preserving features with the typical U-Net, our model detects salient objects accurately. Extensive experiments conducted on six benchmark datasets demonstrate that our method outperforms existing state-of-the-art algorithms in both accuracy and efficiency. Besides, the attribute-based performance on the SOC dataset show that the proposed model ranks first in the majority of challenging scenes. Code can be found at https://github.com/wuzhe71/SCAN.

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Code

wuzhe71/SCAN officialmentioned in paperpytorch report

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Tasks

Attribute Edge Detection Object Object Detection RGB Salient Object Detection Salient Object Detection object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
RGB Salient Object Detection	SOC	SCRN	Average MAE	0.099	#4 of 7	Archive leaderboard	report
RGB Salient Object Detection	SOC	SCRN	S-Measure	0.838	#4 of 7	Archive leaderboard	report
RGB Salient Object Detection	SOC	SCRN	mean E-Measure	0.859	#4 of 7	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Concatenated Skip Connection Convolution Max Pooling ReLU U-Net

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