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GILNet: Grouping interaction learning network for lightweight salient object detection

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Abstract

Recently, salient object detection (SOD) has achieved significant progress with the rapid development of convolutional neural networks (CNNs). However, the enhancement of SOD accuracy often comes at the cost of increased network size and computational complexity, hindering the application of existing SOD methods to lightweight devices, particularly robot devices. To address this issue, we propose a lightweight grouping interaction learning network (GILNet) for efficient and effective multi-level feature learning and shared feature aggregation. Specifically, our novel grouping interaction learning module (GIL) enables efficient feature extraction, and based on this module, we construct a lightweight backbone network to extract multi-scale features. Furthermore, we introduce a shared feature aggregation (SFA) module to aggregate these features in a shared manner, and a progressive guidance prediction (PGP) module to gradually refine the saliency predictions. Extensive experiments on five popular benchmarks demonstrate that GILNet yields comparable accuracy with state-of-the-art methods. More importantly, GILNet operates at a GPU speed of 345 frames/s with only 1.21M parameters, representing a significant reduction in computational cost and model size. These results highlight the significance of our method in achieving a better trade-off between accuracy and efficiency.

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Authors and Affiliations

  1. Software Collage, Northeastern University, Shengyang, 110819, China

    Zhiliang Zhu, Hai Yu & Wei Zhang

  2. School of Artificial Intelligence, Shenyang University of Technology, Shenyang, 110870, China

    Yiru Wei

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  1. Yiru Wei
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  2. Zhiliang Zhu
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  3. Hai Yu
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  4. Wei Zhang
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Contributions

Yiru Wei: Data analysis, Methodology and Writing; Zhiliang Zhu: Formal analysis; Wei Zhang: Validation; Hai Yu: Validation.

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Correspondence to Zhiliang Zhu.

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Wei, Y., Zhu, Z., Yu, H. et al. GILNet: Grouping interaction learning network for lightweight salient object detection. Appl Intell 55, 15 (2025). https://doi.org/10.1007/s10489-024-05860-w

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