PS-net: progressive selection network for salient object detection.

Ren, Jianyi; Wang, Zheng; Ren, Jinchang

Abstract

Low-level features contain abundant details and high-level features have rich semantic information. Integrating multi-scale features in an appropriate way is significant for salient object detection. However, direct concatenation or addition taken by most methods ignores the distinctions of contribution among multi-scale features. Besides, most salient object detection models fail to dynamically adjust receptive fields to fit objects of various sizes. To tackle these problems, we propose a Progressive Selection Network (PS-Net). Specifically, PS-Net dynamically extracts high-level features and encourages high-level features to guide low-level features to suppress the background response of the original features. We proposed a salient model PS-Net that selects features progressively at multiply levels. First, we propose a Pyramid Feature Dynamic Extraction module to dynamically select appropriate receptive fields to extract high-level features by Feature Dynamic Extraction modules step by step. Besides, a Self-Interaction Attention module is designed to extract detailed information for low-level features. Finally, we design a Scale Aware Fusion module to fuse these multiple features for adequate exploitation of high-level features to refine low-level features gradually. Compared with 19 start-of-the-art methods on 6 public benchmark datasets, the proposed method achieves remarkable performance in both quantitative and qualitative evaluation. We performed a lot of ablation studies, and more discussions to demonstrate the effectiveness and superiority of our proposed method. In this paper, we propose a PS-Net for effective salient object detection. Extensive experiments on 6 datasets validate that the proposed model outperforms 19 state-of-the-art methods under different evaluation metrics.

Citation

REN, J., WANG, Z. and REN, J. 2022. PS-net: progressive selection network for salient object detection. Cognitive computation [online], 14(2), pages 794-804. Available from: https://doi.org/10.1007/s12559-021-09952-4

• Journal Article Type: Article
• Acceptance Date: Sep 29, 2021
• Online Publication Date: Jan 16, 2022
• Publication Date: Mar 31, 2022
• Deposit Date: Jul 1, 2022
• Publicly Available Date: Jan 17, 2023
• Journal: Cognitive Computation
• Print ISSN: 1866-9956
• Electronic ISSN: 1866-9964
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 2
• Pages: 794-804
• DOI: https://doi.org/10.1007/s12559-021-09952-4
• Keywords: Salient object detection; Attention mechanism; Multi-scale features
• Public URL: https://rgu-repository.worktribe.com/output/1584536