Questions tagged [fully-convolutional-networks]

For questions related to fully convolutional networks (FCNs), which is formally described in the paper "Fully Convolutional Networks for Semantic Segmentation" (2015) by Jonathan Long et al. An example of an FCN is the U-net (introduced in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation" by Olaf Ronneberger et al.).

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What is a fully convolution network?

I was surveying some literature related to Fully Convolutional Networks and came across the following phrase, A fully convolutional network is achieved by replacing the parameter-rich fully ...
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1answer
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Can a fully convolutional network always return an image of the same size as the original?

I'm trying to perform a segmentation task on images of multiple sizes using fully convolutional neural networks. Currently, I'm using EfficientNet as a feature extractor, and adding a deconvolution/...
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2answers
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Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling?

Does a fully convolutional network share the same translation invariance properties we get from networks that use max-pooling? If not, why do they perform as well as networks which use max-pooling?
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1answer
230 views

What do the words “coarse” and “fine” mean in the context of computer vision?

I was reading the well know paper Fully Convolutional Networks for Semantic Segmentation, and, throughout the whole paper, they talk use the term fine and coarse. I was wondering what they mean. The ...
4
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1answer
430 views

Why can a fully convolutional network accept images of any size?

On this article, it says that: The UNET was developed by Olaf Ronneberger et al. for Bio Medical Image Segmentation. The architecture contains two paths. First path is the contraction path (also ...
4
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2answers
4k views

How to handle rectangular images in convolutional neural networks?

Almost all the convolutional neural network architecture I have come across have a square input size of an image, like $32 \times 32$, $64 \times 64$ or $128 \times 128$. Ideally, we might not have a ...
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5answers
14k views

How can I deal with images of variable dimensions when doing image segmentation?

I'm facing the problem of having images of different dimensions as inputs in a segmentation task. Note that the images do not even have the same aspect ratio. One common approach that I found in ...
2
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1answer
754 views

What are the counterparts of non-linearities and dropout in fully convolutional networks?

I am trying to replicate the fully convolutional networks (FCN) concept described here for semantic segmentation. It seems people have successfully trained such models by removing fully connected ...