Lecture 11 - Segmentation Flashcards

1
Q

What is image segmentation?

A

Image segmentation is the process of partitioning an image into multiple segments or regions to simplify and/or change the representation of an image into something more meaningful and easier to analyze.

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2
Q

Describe semantic segmentation.

A

Semantic segmentation involves classifying each pixel in an image into a class label, assigning a label to every pixel to identify the objects and regions present.

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3
Q

What is a hyper-column in the context of image segmentation?

A

A hyper-column is a representation that combines the layer activations from each level of a convolutional network at a given pixel location, providing a rich, multi-scale feature representation for that pixel.

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4
Q

Explain fully convolutional networks (FCNs) in semantic segmentation.

A

FCNs are networks that replace fully connected layers with convolutional layers, allowing them to take input images of any size and output segmentation maps of the same size, enabling pixel-wise classification.

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5
Q

What is the role of a conditional random field (CRF) in segmentation?

A

CRFs are used to refine the segmentation by modeling the spatial dependencies and relationships between neighboring pixels, often leading to smoother and more accurate segmentation boundaries.

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6
Q

Describe the encoder-decoder structure in segmentation networks.

A

Encoder-decoder structures consist of an encoder that progressively reduces the spatial dimensions of the input to capture context and a decoder that upsamples the reduced representation to produce a dense segmentation map.

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7
Q

What are dense prediction models?

A

Dense prediction models generate output predictions at each pixel location, directly mapping the input to the output at the same resolution, used in tasks like segmentation and depth estimation.

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8
Q

Explain the concept of dilated convolutions.

A

Dilated convolutions involve inserting zeros between the filter weights, effectively expanding the receptive field without increasing the number of parameters, capturing multi-scale context.

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9
Q

What is transfer learning and how is it used in segmentation?

A

Transfer learning involves pre-training a model on a large dataset and then fine-tuning it on a smaller, task-specific dataset. In segmentation, it helps leverage learned features from image classification tasks.

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10
Q

What are the advantages of using the U-Net architecture?

A

The U-Net architecture, originally designed for biomedical image segmentation, features a symmetric encoder-decoder structure with skip connections, providing precise localization and efficient upsampling.

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11
Q

What is the purpose of mean intersection over union (IoU) in segmentation evaluation?

A

Mean IoU is a metric used to evaluate segmentation performance by measuring the overlap between the predicted segmentation and the ground truth, averaged across all classes.

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12
Q

Describe the concept of attention mechanisms in segmentation.

A

Attention mechanisms focus on relevant parts of the input image, enhancing the model’s ability to capture fine details and long-range dependencies, often improving segmentation accuracy.

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13
Q

Write the formula for mean intersection over union (IoU).

A
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14
Q

Provide the formula for the pixel-wise cross-entropy loss used in segmentation.

A
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15
Q

What are the key challenges in image segmentation?

A

Key challenges include handling diverse object scales, occlusions, varying lighting conditions, and ensuring accurate and smooth boundaries in the segmented output.

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16
Q

How do fully convolutional networks (FCNs) differ from traditional convolutional networks?

A

FCNs replace fully connected layers with convolutional layers, allowing them to output segmentation maps that match the input image size, suitable for pixel-wise classification.

17
Q

Explain how transfer learning benefits segmentation tasks.

A

Transfer learning leverages features learned from large datasets in related tasks, reducing the need for extensive labeled data and training time, and often improving segmentation performance.

18
Q

What is the role of bilinear interpolation in upsampling within segmentation networks?

A

Bilinear interpolation is used to increase the spatial resolution of feature maps by linearly interpolating between pixel values, providing smoother upsampling compared to nearest-neighbor methods.

19
Q

Describe the encoder-decoder structure of the U-Net architecture.

A

The U-Net architecture consists of an encoder that downsamples the input image to capture context, followed by a decoder that upsamples the features to produce a high-resolution segmentation map, with skip connections between corresponding layers.

20
Q

What is the significance of using dilated convolutions in segmentation networks?

A

Dilated convolutions increase the receptive field without adding extra parameters, allowing the network to capture multi-scale context and improve segmentation accuracy, especially for large objects.

21
Q

How does a conditional random field (CRF) refine segmentation results?

A

CRFs model the dependencies between neighboring pixels, refining segmentation by enforcing spatial coherence and producing smoother and more accurate boundaries.

22
Q

What are hyper-columns and how are they used in segmentation?

A

Hyper-columns are multi-scale representations that combine activations from different layers of a network at each pixel, providing rich features for precise segmentation.

23
Q

Explain the concept of mean field approximation in the context of CRFs.

A

Mean field approximation is a technique for approximating the inference in CRFs, often used to make the computation tractable by iteratively updating the marginal distributions of the variables.

24
Q

What are the advantages of using pyramid scene parsing networks (PSPNet) in segmentation?

A

PSPNet captures global context information by pooling features at multiple scales, improving the segmentation of objects at different scales and enhancing overall accuracy.

25
Q

How do attention mechanisms enhance segmentation models?

A

Attention mechanisms focus on the most relevant parts of the input, allowing the model to capture fine details and long-range dependencies, often leading to more accurate and detailed segmentation.

26
Q

Describe the role of context modules in segmentation networks.

A

Context modules aggregate information from different regions of the image, helping the network understand the relationship between objects and their surroundings, improving segmentation performance.