BBS-Net: RGB-D Salient Object Detection with a Bifurcated Backbone Strategy Network

By Deng-Ping Fan, Yingjie Zhai, Ali Borji, Jufeng Yang, Ling Shao. European Conference on Computer Vision, 2020

390 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d4ecd47fedfd037bcba5337fb7691978392e5d10

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Nian Liu
2. Junwei Han
3. Ni Zhang
4. Zhengyi Liu
5. Kaiyuan Wan
6. Yacheng Tan
7. Qian He
8. Yun Xiao

Citations in the corpus

• Visual Saliency Transformer (2021, methodology): “Some works introduced the attention mechanism to learn more discriminative features, including spatial and channel attention [52, 80, 16, 7] or pixel-wise contextual attention [37].”
• SwinNet: Swin Transformer Drives Edge-Aware RGB-D and RGB-T Salient Object Detection (2022, methodology): “Furthermore, we replace spatial alignment and channel re-calibration module with Depth-enhanced Module (DEM) in BBS-Net [39] which consists of the similar channel attention and spatial attention but no alignment operation to verify the effectiveness of spatial alignment and channel re-calibration mo”