FCN (Fully Convolutional Network) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a Fully Convolutional Network (FCN)?

An FCN is a type of neural network made only of convolutional layers. It can take images of any size and produce output maps of corresponding size, useful for tasks like image segmentation.

beginner

Why do FCNs not use fully connected layers?

FCNs replace fully connected layers with convolutional layers to keep spatial information and allow inputs of varying sizes, enabling pixel-wise predictions.

intermediate

How does an FCN produce pixel-wise predictions?

FCNs use convolutional layers to create feature maps and then upsample these maps (using methods like transposed convolution) to match the input image size, giving predictions for each pixel.

intermediate

What is the role of upsampling in FCNs?

Upsampling increases the spatial size of feature maps to the original image size, allowing the network to output detailed, pixel-level predictions for tasks like segmentation.

beginner

Name a common application of FCNs in computer vision.

FCNs are commonly used for semantic segmentation, where each pixel in an image is classified into a category, like labeling roads, cars, or people.

What type of layers does an FCN primarily use?

A Convolutional layers

B Fully connected layers

C Recurrent layers

D Pooling layers only

FCNs use convolutional layers instead of fully connected layers to maintain spatial information.

Why are fully connected layers avoided in FCNs?

A They are slower to train

B They increase model size unnecessarily

C They cause overfitting

D They reduce spatial information

Fully connected layers flatten spatial data, losing location information needed for pixel-wise tasks.

What is the purpose of upsampling in an FCN?

A To reduce the number of channels

B To increase spatial resolution of feature maps

C To add non-linearity

D To normalize the data

Upsampling restores the feature map size to match the input image for pixel-level predictions.

Which task is an FCN best suited for?

A Semantic segmentation

B Image classification

C Text generation

D Speech recognition

FCNs produce pixel-wise outputs, ideal for semantic segmentation.

How does an FCN handle input images of different sizes?

A It resizes all images to a fixed size

B It uses fully connected layers to adapt

C It processes images directly using convolutional layers

D It cannot handle different sizes

Convolutional layers allow FCNs to accept variable-sized inputs without resizing.

Explain how a Fully Convolutional Network (FCN) differs from a traditional CNN in terms of architecture and output.

Think about how FCNs keep spatial information and what that means for the output.

Describe the role of upsampling in FCNs and why it is important for tasks like semantic segmentation.

Consider what happens after convolution reduces image size.