Stacked Cross Refinement Network for Edge-Aware Salient Object Detection

Citations Over TimeTop 1% of 2019 papers

Abstract

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.

Related Papers

• Keeping Conflicts Latent: "Salient" versus "Non-Salient" Interpersonal Conflict Management Strategies of Japanese(2013)
• → Solutions to the Third Benchmark Control Problem(1991)3 cited
• Theoretical Analysis of the Benchmark for Choosing Manipulative Instruments of Monetary Policies(2009)
• → Exploring disk performance benchmarks(2017)
• → Support Structure Performance Benchmark(2023)