Spatial context-aware network for salient object detection. (June 2021) - Kong, Yuqiu; Feng, Mengyang; Li, Xin; Lu, Huchuan; Liu, Xiuping; Yin, Baocai
Highlights: An SCA-Net that explicitly explores and integrates global and local contextual information. An LPCM embedded in the endcoding network to grant coarse-scale feature maps better discrimination ability. An SPCM in feature decoding to help integrate multi-scale feature maps and compensate saliency predictions with fine detailed cues. Abstract: 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 feature 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. Extensive experiments on five large-scale benchmarks demonstrate that SCA-Net achieves favorable performance against very recent state-of-the-art algorithms.