Semantic Segmentation Deep Neural Network | What is Semantic Segmentation by Dr Arshad Afridi
453 بار بازدید -
2 سال پیش
-
In this video, we study
In this video, we study the basic concept of segmentation. We then discuss in the detail concept of semantic segmentation. Semantic Segmentation Deep Neural Network is explained step by step. We study that what is Semantic Segmentation and how Semantic Segmentation works. What are the applications of semantic Segmentation. Why Semantic Segmentation is important.
Semantic Segmentation Label every pixel. It does not differentiate instances (cows). Classic computer vision problem.
Instance Segmentation: Detect instances, give category, label pixels. “simultaneous detection and segmentation” (SDS). Lots of recent work (MS-COCO).
Semantic Segmentation Extract patch Run through a CNN and Classify center pixel and Repeat for every pixel. Run “fully convolutional” network to get all pixels at once. it gives Smaller output due to pooling.
Semantic Segmentation: Multi-Scale: External “bottom-up” segmentation
, Resize image to multiple scales, Run one CNN per scale, Upscale outputs and concatenate, Combine everything for final outputs.
Semantic Segmentation: Refinement: Apply CNN once to get labels, Apply AGAIN to refine labels, Same CNN weights: recurrent convolutional network,
More iterations improve results.
Semantic Segmentation: Upsampling, Learnable upsampling! “skip connections”. Skip connections = Better results.
Learnable Upsampling: “Deconvolution”. Typical 3 x 3 convolution, stride 1 pad 1 Dot product between filter and input.
Learnable Up sampling: “Deconvolution”, Input gives weight for filter.
Same as backward pass for normal convolution! “Deconvolution” is a bad name, already defined as “inverse of convolution” Better names:
convolution transpose, backward stride convolution, 1/2 strided convolution, up convolution.
Convolution and De convolution examples are Normal VGG and “Upside down” VGG.
Semantic Segmentation, Segmentation in Deep Neural Network, Segmentation DL, What is Semantic Segmentation, Segmentation by Dr Arshad Afridi, basic concept of segmentation, Semantic Segmentation Refinement, Semantic Segmentation Upsampling, Convolution and De convolution, backward stride convolution, Learnable Upsampling: Deconvolution, Upsampling, Learnable upsampling, convolution transpose, simultaneous detection and segmentation, Semantic Segmentation Multi-Scale, Segmentation Label every pixel
#SemanticSegmentation #Segmentation #DeepLearning
Semantic Segmentation Label every pixel. It does not differentiate instances (cows). Classic computer vision problem.
Instance Segmentation: Detect instances, give category, label pixels. “simultaneous detection and segmentation” (SDS). Lots of recent work (MS-COCO).
Semantic Segmentation Extract patch Run through a CNN and Classify center pixel and Repeat for every pixel. Run “fully convolutional” network to get all pixels at once. it gives Smaller output due to pooling.
Semantic Segmentation: Multi-Scale: External “bottom-up” segmentation
, Resize image to multiple scales, Run one CNN per scale, Upscale outputs and concatenate, Combine everything for final outputs.
Semantic Segmentation: Refinement: Apply CNN once to get labels, Apply AGAIN to refine labels, Same CNN weights: recurrent convolutional network,
More iterations improve results.
Semantic Segmentation: Upsampling, Learnable upsampling! “skip connections”. Skip connections = Better results.
Learnable Upsampling: “Deconvolution”. Typical 3 x 3 convolution, stride 1 pad 1 Dot product between filter and input.
Learnable Up sampling: “Deconvolution”, Input gives weight for filter.
Same as backward pass for normal convolution! “Deconvolution” is a bad name, already defined as “inverse of convolution” Better names:
convolution transpose, backward stride convolution, 1/2 strided convolution, up convolution.
Convolution and De convolution examples are Normal VGG and “Upside down” VGG.
Semantic Segmentation, Segmentation in Deep Neural Network, Segmentation DL, What is Semantic Segmentation, Segmentation by Dr Arshad Afridi, basic concept of segmentation, Semantic Segmentation Refinement, Semantic Segmentation Upsampling, Convolution and De convolution, backward stride convolution, Learnable Upsampling: Deconvolution, Upsampling, Learnable upsampling, convolution transpose, simultaneous detection and segmentation, Semantic Segmentation Multi-Scale, Segmentation Label every pixel
#SemanticSegmentation #Segmentation #DeepLearning
2 سال پیش
در تاریخ 1401/03/11 منتشر شده
است.
453
بـار بازدید شده