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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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Why the architeture of Resnet18 is suitable to images classification?

I am studying convolutional networks and in particular I have focused on the ResNet18 network. I've been studying ResNet18 and understand the purpose of skip connections and residual network. However,...
Domme's user avatar
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1 vote
1 answer

Why do we do need compression in Semantic Segmentation?

When doing semantic segmentation, we often make use of FCN, which can be thought of in two parts: an encoder and decoder. As I understand, the encoder compresses the image into a spatially small, but ...
Dude156's user avatar
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How to compensate the receptive field offset in a Fully Convolutionl Network?

I'm studying the Fully Convolutional Networks right now and when it's clear that the receptive field is not dependent on the input size (the whole network in a way is independent from the input size), ...
Antoni's user avatar
  • 101
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1 answer

Can a fully convolutional network handle smaller images at test time?

It is said that a fully convolutional network can handle any image size. I don't understand. Unlike regular CNN, a fully convolutional network reinterprets the dense layer as a convolutional operation....
Tom Bennett's user avatar
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1 answer

Why FCNN is not always better than CNN?

Why Fully-Connected Neural Network is not always better than Convolutional Neural Network? FCNN is easily overfitting due to many params, then why didn't it reduce the params to reduce overfitting. If ...
Muhammad Ikhwan Perwira's user avatar
2 votes
1 answer

Best practice for handling letterboxed images for non fully-convolutional deep learning networks?

I'm working on a depth estimation network. It has two outputs: A relative depth map A scalar for scaling the relative depth map into an absolute depth map. This second output uses dense layers so ...
NateW's user avatar
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1 vote
0 answers

FCNs: Questions about the filter rarefaction in the CVPR paper [Long et al., 2015]

I am reading the paper about the fully convolutional network (FCN). I had some questions about the part where the authors discuss the filter rarefaction technique (I guess this is roughly equivalent ...
scho's user avatar
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Computational complexity of a CNN network

In the following network, the convolution operations of convolutional blocks are performed by three 1-D kernels with the sizes 8, 5, and 3 respectively along with stride equal to 1. The final network ...
Quantam's user avatar
1 vote
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In the DeepView paper, do they use the same FCN for all depth slices AND all views?

I'm trying to replicate a paper from Google on view synthesis/lightfields from 2019: DeepView: View Synthesis with Learned Gradient Descent and this is the PDF. Basically the input to the neural ...
DonDrapper's user avatar
20 votes
1 answer

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 ...
r4bb1t's user avatar
  • 335
8 votes
2 answers

What is the point of using 1D and 2D convolutions with a kernel size of 1 and 1x1 respectively?

I understand the gist of what convolutional neural networks do and what they are used for, but I still wrestle a bit with how they function on a conceptual level. For example, I get that filters with ...
Arcturai's user avatar
2 votes
1 answer

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/...
Alex's user avatar
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3 votes
3 answers

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?
FourierFlux's user avatar
4 votes
1 answer

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 ...
Charlie Parker's user avatar
7 votes
1 answer

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 ...
SDG's user avatar
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10 votes
2 answers

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 ...
Santhosh Dhaipule Chandrakanth's user avatar
33 votes
5 answers

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 ...
MattSt's user avatar
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2 votes
1 answer

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 ...
abhinavkulkarni's user avatar