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Spatial context-aware network for salient object detection
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DOI：10.1016/j.patcog.2021.107867.png)
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Salient Object Detection (SOD) is a fundamental problem in the field of computer vision. This paper presents a novel Spatial Context-Aware Network (SCA-Net) for SOD in images. Compared with other recent deep learning based SOD algorithms, SCA-Net can more effectively aggregate multi-level deep features. A Long-Path Context Module (LPCM) is employed to grant better discrimination ability to fea-ture maps that incorporate coarse global information. Consequently, a more accurate initial saliency map can be obtained to facilitate subsequent predictions. SCA-Net also adopts a Short-Path Context Module (SPCM) to progressively enforce the interaction between local contextual cues and global features. Ex-tensive experiments on five large-scale benchmarks demonstrate that SCA-Net achieves favorable perfor-mance against very recent state-of-the-art algorithms. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Salient object detection
Context-aware methods
Deep learning
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