CNN - Convolutional Neural Networks Semantic Segmentation Questions and Answers Flashcards

What is the goal of semantic segmentation in computer vision?

Answer: Assigning a class label to every pixel in an image

Semantic segmentation classifies each pixel into a predefined category, producing a dense prediction map that delineates different regions of the image.

What key innovation did Fully Convolutional Networks (FCN) introduce for semantic segmentation?

Answer: Replacing fully connected layers with convolutional layers to enable end-to-end pixel-wise prediction

FCN replaced the fully connected layers of classification networks with convolutional layers, allowing the network to accept arbitrary-sized inputs and produce spatial output maps.

What architecture is U-Net primarily designed for, and what is its signature structural feature?

Answer: Biomedical image segmentation with a symmetric encoder-decoder and skip connections

U-Net is a symmetric encoder-decoder network with skip connections that directly concatenate feature maps from the encoder to the corresponding decoder level, preserving fine-grained spatial detail.

What is the role of skip connections in the U-Net architecture?

Answer: They pass high-resolution feature maps from the encoder to the decoder to recover spatial detail lost during downsampling

Skip connections concatenate encoder feature maps with decoder feature maps at matching resolutions, allowing the decoder to recover fine spatial details that are lost during pooling.

What is the fundamental difference between semantic segmentation and instance segmentation?

Answer: Semantic segmentation assigns the same label to all pixels of a class, while instance segmentation distinguishes individual object instances

In semantic segmentation all pixels of the same class share one label, whereas instance segmentation distinguishes separate instances so two cars get different masks.

What is the encoder-decoder architecture in CNN-based segmentation networks?

Answer: A network where the encoder downsamples the input to a bottleneck and the decoder upsamples it back to the original resolution for pixel prediction

The encoder extracts hierarchical features while reducing spatial resolution, and the decoder gradually upsamples these features back to the input resolution to produce the segmentation map.