Paper Review: U2-Net Going Deeper with Nested U-Structure for Salient Object Detection
Segmenting different objects in a given image has been a pretty well-known task in the field of computer vision. Over the years we have seen autoencoders to crazy deep learning models like Deeplab being used for semantic segmentation. In the deep ocean of all the models, one name still remains at the top, and it's called U-Net. U-Net was released in 2018 and since then it has gained huge popularity and been used in some form or the other for several different tasks related to segmentation. In this blog, we are going to cover one variant of U-net called U²-Net or U-squared Net. U²-Net is basically a U-Net made of U-Net.
So, without further ado, let’s jump into this awesome paper. U²-Net was designed for the purpose of saliency object detection or SOD. For those who don’t know, saliency object detection is basically detecting the most important or the main object in a given image.
Key developments
The architecture of our U2 -Net is a two-level nested U-structure. The design has the following advantages:
- It is able to capture more contextual information from different scales thanks to the mixture of receptive fields of different sizes in our proposed ReSidual U-blocks (RSU).
