Electrical engineering

Stereoscopy

Pattern recognition (psychology)

Salient

Operating system

Algorithm

Fuse (electrical)

Engineering

Artificial intelligence

Set (abstract data type)

Programming language

Pedestrian

Transport engineering

Object detection

Code (set theory)

Overhead (engineering)

Pedestrian detection

Computer science

Computation

Computer vision

Convolutional neural network

IEEE Transactions on Image Processing

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 ×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/.

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