U2Net

SOD

SOD (Salient Object Detection) is a topics in deep learning that by given a image, SOD can automatically segmentize the most interested objects of the image without any hints. SOD learns how human see the interested objects by detecting the denisity of feature points and segmentize the most dense parts. So far, U2Net provide a state of art performance.

First results of U2Net

These are the first results of the U2Net on target benchmark images. For the full results can be checked in Chimay-SOD1 and asubset Chimay-SOD2 can be found.

Image sliders

In this page, image slider for jekyll and its js code is used for image slider. Also a Jekyll Ideal Image Slider Include Demo shows the possiblity of Ideal Image Slider.

References

• 那些一鍵摳圖的軟件是怎麼做到的？這些語義分割方法了解一下
• 顯著性檢測數據集—學習筆記
• Salient Object Detection
• awesome-segmentation-saliency-dataset
• Salient Objects Dataset (SOD)
• What are Autoencoders?
• [魔法陣系列] AutoEncoder 之術式解析
• U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection