Multiplexing Multi-Scale Features Network for Salient Target Detection
Source abstract
Abstract
This paper proposes a multiplexing multi-scale features network (MMF-Network) for salient target detection to tackle the issue of incomplete detection structures when identifying salient targets across different scales. The network, based on encoder–decoder architecture, integrates a multi-scale aggregation module and a multi-scale visual interaction module. Initially, a multi-scale aggregation module is constructed, which, despite potentially introducing a small amount of noise, significantly enhances the high-level semantic and geometric information of features. Subsequently, SimAM is employed to emphasize feature information, thereby highlighting the significant target. A multi-scale visual interaction module is designed to enable compatibility between low-resolution and high-resolution feature maps, with dilated convolutions utilized to expand the receptive field of high-resolution feature maps. Finally, the proposed MMF-Network is tested on three datasets: DUTS-Te, HUK-IS, and PSCAL-S, achieving scores of 0.887, 0.811, and 0.031 in terms of its F-value SSIM and MA, respectively. The experimental results demonstrate that the MMF-Network exhibits a superior performance in salient target detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Visual Attention and Saliency Detection · Advanced Neural Network Applications · Infrared Target Detection Methodologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electronic Information Engineering, Liaoning Technical University, Huludao 125105, China
School of Electronic and Electrical Engineering, Bohai shipbuilding Vocational College, Huludao 125105, China
Funding
National Natural Science Foundation of China · Liaoning Provincial Colleges and Universities Basic Scientific Research Project
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