BASNet (CVPR 2019, OD)

2024. 6. 14. 15:54·DL·ML/Paper

Figure 1: Sample result of BASNet and other methods.

 

기존 saliency map을 얻는 architecture는 여전히 fine structure를 포착하는 데 어려움이 있어 이를 해결하기 위한 ConvNet 기반 architecture를 제시한다(See Fig. 1). 이때 refinement network는 U-Net shape의 residual refinement module이다. 이를 train하기 위해서 BCE와 SSIM(Structural SIMilarity)[1], IoU가 사용된다. 

 

Figure 2: Overall architecture of BASNet.

 

BASNet의 전체 architecture는 Fig. 2에 제시되어 있다. 먼저 saliency map을 predict하는 module이 있고, 뒤에 multi-scale residual refinement module이 붙어서 결과를 refine하는 형태이다. 

 

 

 

 


References

[1] Zhou Wang, Eero P Simoncelli, and Alan C Bovik. Multi-scale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, volume 2, pages 1398–1402. Ieee, 2003

Footnotes

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