RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images

@article{Cong2021RRNetRR,
  title={RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images},
  author={Runmin Cong and Yumo Zhang and Leyuan Fang and Jun Li and Chunjie Zhang and Yao Zhao and Sam Tak Wu Kwong},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.14223}
}
Résumé—Salient object detection (SOD) for optical remote sensing images (RSIs) aims at locating and extracting visually distinctive objects/regions from the optical RSIs. Despite some saliency models were proposed to solve the intrinsic problem of optical RSIs (such as complex background and scale-variant objects), the accuracy and completeness are still unsatisfactory. To this end, we propose a relational reasoning network with parallel multi-scale attention for SOD in optical RSIs in this… 
Multi-Content Complementation Network for Salient Object Detection in Optical Remote Sensing Images
  • Gongyang Li, Zhi Liu, Weisi Lin, Haibin Ling
  • Computer Science, Engineering
    IEEE Transactions on Geoscience and Remote Sensing
  • 2021
TLDR
A novel Multi-Content Complementation Network (MCCNet) is proposed to explore the complementarity of multiple content for RSI-SOD, based on the general encoder-decoder architecture, and contains a novel key component named MCCM, which bridges the encoder and the decoder.

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