Day-20 Computer Vision Learning Deeplabv1: Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs by Google Follow me for similar post : 🇮🇳 Ashish Patel Interesting Facts 🔸 Deeplabv1 used new type Aproaches such as Atrous Convolution and Fully Connected Conditional Random Field (CRF) 🔸 Deeplabv1 use VGG as backbone layer. 🔸 Deeplabv1 : 2893 Citation Published in ICLR2015. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/eyB-Dzf Tensorflow : https://bit.ly/391bnT4, https://bit.ly/3iuKKsM Pytorch : https://bit.ly/38XCoXq ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Atrous Convolution : The term “Atrous” indeed comes from French “à trous” meaning hole. Thus, it is also called “algorithme à trous” and “hole algorithm”. Some of the papers also call this “dilated convolution”. It is commonly used in wavelet transform and right now it is applied in convolutions for deep learning. #innovation #artificialintelligence #computervision
Fully connected CRFs 🔸 Traditional CRFs are applied to smooth noise segmentation maps, which can remove suspected weak class prediction values. Applying CRFs to smooth the edges is contrary to our restoration of detailed structural information. The author applies a fully connected CRF model to overcome the limitations of CRFs. Multi-scale prediction 🔸 Multi-scale prediction methods are applied to increase the accuracy of edge positioning, and the feature information of the first and second layers is directly connected with the feature layer of the last layer and sent to the softmax layer. #machinelearning #data #analytics
Atrous Algorithm 🔸 The hole strategy has been widely used in wavelet transform. This method allows us to calculate CNN features with arbitrary ratio values and does not need to quote arbitrary approximate values. #technology #india #deeplearning
🔸 The author explained that there are two technical difficulties that need to be overcome when DCNNs are applied to image tasks. 🔸 One is signal downsampling and the other is space insensitivity. Signal downsampling is mainly due to the constant pooling and downsampling in the calculation process. 🔸 The spatial insensitivity is due to the need for spatial fixation in the classification task, which limits the accuracy of the DCNNs model. To solve this problem, the author uses a fully connected conditional random field. 🔸 The fully connected CRF has high computational efficiency and can obtain edge detail information.
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