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CNN - Convolutional Neural Networks Semantic Segmentation Questions and Answers Flashcards

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  1. What are dilated (atrous) convolutions and why are they used in semantic segmentation?

    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.

  2. What is the Atrous Spatial Pyramid Pooling (ASPP) module used for in DeepLab?

    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.

  3. What does the mean Intersection over Union (mIoU) metric measure in semantic segmentation evaluation?

    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.

  4. What is Panoptic Segmentation?

    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.

  5. What is the Pyramid Scene Parsing Network (PSPNet) and what problem does it address?

    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.

  6. What loss function is most commonly used as the primary training objective in semantic segmentation CNNs?

    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.

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