SAMNet: Stereoscopically Attentive Multi-Scale Network for Lightweight Salient Object Detection

Recent progress on salient object detection (SOD) mostly benefits from the explosive development of Convolutional Neural Networks (CNNs). However, much of the improvement comes with the larger network size and heavier computation overhead, which, in our view, is not mobile-friendly and thus difficul...

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Bibliographic Details
Published in:IEEE transactions on image processing 2021, Vol.30, p.3804-3814
Main Authors: Liu, Yun, Zhang, Xin-Yu, Bian, Jia-Wang, Zhang, Le, Cheng, Ming-Ming
Format: Article
Language:English
Subjects:
Artificial neural networks
Deep learning
Explosives detection
Fuses
Lightweight
lightweight saliency detection
Lightweight salient object detection
Modules
multi-scale learning
Object detection
Object recognition
Salience
Semantics
Source code
Stereo image processing
Task analysis
Visualization
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Summary:Recent progress on salient object detection (SOD) mostly benefits from the explosive development of Convolutional Neural Networks (CNNs). However, much of the improvement comes with the larger network size and heavier computation overhead, which, in our view, is not mobile-friendly and thus difficult to deploy in practice. To promote more practical SOD systems, we introduce a novel Stereoscopically Attentive Multi-scale (SAM) module, which adopts a stereoscopic attention mechanism to adaptively fuse the features of various scales. Embarking on this module, we propose an extremely lightweight network, namely SAMNet, for SOD. Extensive experiments on popular benchmarks demonstrate that the proposed SAMNet yields comparable accuracy with state-of-the-art methods while running at a GPU speed of 343fps and a CPU speed of 5fps for 336 \times 336 inputs with only 1.33M parameters. Therefore, SAMNet paves a new path towards SOD. The source code is available on the project page https://mmcheng.net/SAMNet/ .
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2021.3065239