/BGRDNet

Primary LanguagePython

BGRDNet: RGB-D Salient Object Detection with a Bidirectional Gated Recurrent Decoding Network

The paper has been published in Multimedia Tools and Applications. The detail can be seen in paper

Abstract:

Traditional U-Net framework generates multi-level features by the successive convolution and pooling operations, and then decodes the saliency cue by progressive upsampling and skip connection. The multi-level features are generated from the same input source, but quite different with each other. In this paper, we explore the complementarity among multi-level features, and decode them by Bi-GRU. Since multi-level features are different in the size, we first propose scale adjustment module to organize multi-level features into sequential data with the same channel and resolution. The core unit SAGRU of Bi-GRU is then devised based on self-attention, which can effectively fuse the history and current input. Based on the designed SAGRU, we further present the bidirectional decoding fusion module, which decoding the multi-level features in both down-top and top-down manners. The proposed bidirectional gated recurrent decoding network is applied in the RGB-D salient object detection, which leverages the depth map as a complementary information. Concretely, we put forward depth guided residual module to enhance the color feature. Experimental results demonstrate our method outperforms the state-of-the-art methods in the six popular benchmarks. Ablation studies also verify each module plays an important role.

Pretraing

链接:https://pan.baidu.com/s/1bxO0ygO3VXUjPXntleVNtg 提取码:b9yd

Training Set

2185 https://drive.google.com/file/d/1fcJj4aYdJ6N-TvvxSZ_sBo-xhtd_w-eJ/view?usp=sharing

2985 https://drive.google.com/file/d/1mYjaT_FTlY4atd-c0WdQ-0beZIpf8fgh/view?usp=sharing

Result Saliency Maps

链接:https://pan.baidu.com/s/1U4OlkgR9E2p7sx4Ye99Cyg 提取码:ehnz

Citation

If you find the information useful, please consider citing:

@article{liu2022bgrdnet,
  title={BGRDNet: RGB-D salient object detection with a bidirectional gated recurrent decoding network},
  author={Liu, Zhengyi and Wang, Yuan and Zhang, Zhili and Tan, Yacheng},
  journal={Multimedia Tools and Applications},
  pages={1--21},
  year={2022},
  publisher={Springer}
}