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Hypergraph attentional convolutional neural network for salient object detection

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Abstract

Learning discriminative features and mining salient visual patterns play an important role in salient object detection (SOD) task. Existing SOD methods suffer from limited receptive field and insufficient cross-level feature mining. To this end, we propose a hypergraph attentional convolutional neural network for SOD task. Specifically, our method consists of (1) an attention based feature fusion module, which efficiently fuses lower layer as well as higher layer features, (2) a hypergraph-based long-range dependency encoder, which enhances the receptive field and global context for detection model, (3) a feature refinement layer, which highlights discriminative features and fuses attentional inputs, and (4) a dual iterative feature propagation decoder, which propagates features and upscale lower level feature maps to higher resolution. Both qualitative and quantitively experiments on public datasets verify the effectiveness of our proposed method. Compared with previous works, our model plays favorably against the state-of-the-arts methods.

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  1. Department of Automation, College of Information Science and Engineering, China University of Petroleum, Beijing, China

    Ze-yu Liu & Jian-wei Liu

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Liu, Zy., Liu, Jw. Hypergraph attentional convolutional neural network for salient object detection. Vis Comput 39, 2881–2907 (2023). https://doi.org/10.1007/s00371-022-02499-x

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