Day-30 Computer Vision Learning 𝗥𝗲𝗳𝗶𝗻𝗲𝗡𝗲𝘁 — Multi-path Refinement Network (Semantic Segmentation) by The University of Adelaide, and Australian Centre for Robotic Vision Follow me for similar post : 🇮🇳 Ashish Patel Interesting Facts : 🔸 It is published in 2017 #CVPR, which has already got over 1499 citations 🔸 A generic multi-path refinement network that explicitly exploits all the information available along the down-sampling process to enable high-resolution prediction using long-range residual connections. 🔸 The deeper layers that capture high-level semantic features can be directly refined using fine-grained features from earlier convolutions. 🔸 A chained residual pooling is also introduced which captures rich background context in an efficient manner. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/etCDUBf Pytorch : https://bit.ly/3co3ZTL Tensorflow : https://bit.ly/3j2o4Av Keras : https://bit.ly/2Yve2hG ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Refinenet used residual network of Resnet but without batch normalization #artificialintelligence #computervision #technology #india
Previous post at github : https://github.com/ashishpatel26/365-Days-Computer-Vision-Learning-Linkedin-Post
🔸 Chained Residual Pooling : The output feature maps of all pooling blocks are fused together with the input feature map through summation of residual connections. It aims to capture background context from a large image region. 🔸 Type of RefineNet : Single, 2-cascaded, 4-cascaded, 4-cascaded 2 scale
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