CNN CNN - Convolutional Neural Networks Semantic Segmentation Questions and Answers 2 — Questions and Answers
Question 1: What are dilated (atrous) convolutions and why are they used in semantic segmentation?
- Convolutions with small kernels used to reduce computation
- Convolutions with gaps between kernel elements that expand the receptive field without increasing parameters or losing resolution (Correct answer)
- Convolutions applied to dilated images
- Convolutions used to separate color channels
Correct answer: Convolutions with gaps between kernel elements that expand the receptive field without increasing parameters or losing resolution
Dilated convolutions insert zeros between kernel weights (dilation rate > 1), allowing the filter to cover a larger area of the input without downsampling, which is critical for maintaining spatial resolution in segmentation.
Question 2: What is the Atrous Spatial Pyramid Pooling (ASPP) module used for in DeepLab?
- Pooling features across the batch dimension
- Capturing multi-scale context by applying parallel dilated convolutions with different dilation rates (Correct answer)
- Applying attention across spatial locations
- Reducing the number of channels before classification
Correct answer: Capturing multi-scale context by applying parallel dilated convolutions with different dilation rates
ASPP applies several dilated convolutions with different rates in parallel and concatenates the results, allowing the network to capture object context at multiple scales simultaneously.
Question 3: What does the mean Intersection over Union (mIoU) metric measure in semantic segmentation evaluation?
- The average pixel accuracy across all images
- The average of per-class IoU scores, where IoU measures overlap between predicted and ground-truth masks (Correct answer)
- The mean number of correctly detected objects
- The ratio of true positives to total predictions
Correct answer: The average of per-class IoU scores, where IoU measures overlap between predicted and ground-truth masks
mIoU computes IoU (intersection divided by union of predicted and ground-truth regions) for each class and averages them, providing a balanced measure across all classes including rare ones.
Question 4: What is Panoptic Segmentation?
- Segmentation applied to panoramic images only
- A unified task that combines semantic segmentation (stuff) and instance segmentation (things) into a single cohesive map (Correct answer)
- A multi-view segmentation approach
- Segmentation applied exclusively to medical images
Correct answer: A unified task that combines semantic segmentation (stuff) and instance segmentation (things) into a single cohesive map
Panoptic segmentation assigns each pixel both a semantic class label and an instance ID where applicable, unifying the 'stuff' categories of semantic segmentation with the 'things' categories of instance segmentation.
Question 5: What is the Pyramid Scene Parsing Network (PSPNet) and what problem does it address?
- A network for parsing text scenes
- A segmentation network that uses a Pyramid Pooling Module to capture global and local context at multiple scales (Correct answer)
- A recurrent network for sequential scene understanding
- A network that parses 3D point clouds
Correct answer: A segmentation network that uses a Pyramid Pooling Module to capture global and local context at multiple scales
PSPNet's Pyramid Pooling Module aggregates features at four different scales using adaptive average pooling, enabling the network to incorporate global context which reduces misclassification from local ambiguity.
Question 6: What loss function is most commonly used as the primary training objective in semantic segmentation CNNs?
- Mean Squared Error (MSE)
- Pixel-wise Cross-Entropy Loss (Correct answer)
- Triplet Loss
- Hinge Loss
Correct answer: Pixel-wise Cross-Entropy Loss
Pixel-wise cross-entropy treats each pixel as an independent classification problem and sums the cross-entropy loss over all pixels, making it the standard choice for semantic segmentation training.
CNN CNN - Convolutional Neural Networks Semantic Segmentation Questions and Answers 2
What are dilated (atrous) convolutions and why are they used in semantic segmentation?