Day-21 Computer Vision Learning CRF-RNN: CRF-RNN — Conditional Random Fields as Recurrent Neural Networks (Semantic Segmentation) by University of Oxford, Stanford University, and Baidu, Inc. 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 🔸 CRF is one of the most successful graphical models in computer vision. 🔸 It is found that Fully Convolutional Network (FCN) outputs a very coarse segmentation results. 🔸 In CRF-RNN, authors proposed to formulate CRF as RNN so that they can integrated with FCN and train the whole network in an end-to-end manner to obtain a better results. 🔸 It is a 2015 ICCV paper with over 2230 citations. 🔸 CRF used Post Processing in Deeplabv1 and Deeplabv2 ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/enQQKiU official : https://lnkd.in/einEFjc Keras: https://bit.ly/3p8f7aU Tensorflow : https://bit.ly/39V5lm8 Pytorch : https://bit.ly/3p4qv7D ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 The purpose of CRF is to refine the coarse output based on the label at each location itself, and the neighboring positions’ labels and locations. #innovation #artificialintelligence #computervision
I am doing fine and able to train an object detector with reasonable map and i have the feeling i finally understand. And then comes an article like this and crushes my view(mental model). Thank you very much! (i mean it). Time to catch up again :-)
🔸This is one of the best model which have achieved highest mAP value on COCO datasets.
As an Innovative and Creative…•7K followers
5yAwesome Ashish, thanks for sharing, are there any negative tradeoffs with this approach?