Answers.Body: <h3>tl;dr</h3>
  
  <blockquote>
    <p>What does that mean in the context of this paper?</p>
  </blockquote>
  
  <p>With "coarse segmentation" the author means a segmentation that doesn't have much detail. "Fine segmentation", on the other hand, refers to a segmentation with a high level of detail.</p>
  
  <blockquote>
    <p>But also more importantly <em>[what does that mean in the context of]</em> general computer vision?</p>
  </blockquote>
  
  <p>The most common use in CV is to describe how general or specific a class in is classification. A "coarse class" is a very broad one, while a "fine class" is a very specific one.</p>
  
  <h3>Intended use</h3>
  
  <p>What the author refers to is the <strong>level of detail</strong> of the resulting segmentation. </p>
  
  <p>A <strong>coarse</strong> segmentation would mean that we have large blobs covering each class without much detail. On the other hand, a <strong>fine</strong> segmentation would have a much higher level of detail which can even go down to pixel level (i.e. pixel-by-pixel correct segmentation).</p>
  
  <p>To make this clear look at the following two examples. As we go from the left to right, the segmentation maps go from coarse to fine:</p>
  
  <p><img src="https://i.sstatic.net/tamtb.png" alt="">
  <img src="https://i.sstatic.net/vT8Pe.png" alt=""></p>
  
  <p>Note that in the rightmost images the result an almost pixel-perfect (i.e. fine details) segmentation map, while the ones on the left don't have much detail and can be considered coarse.</p>
  
  <h3>Alternative use</h3>
  
  <p>Because this isn't an established terminology, some of the times coarse and fine can refer to the nature of the classes in a classification task. Take the top image for example; the label for a coarse classification task could be a <em>tree</em>. For a fine classification task we would have labels like <em>oak tree</em>, <em>pine tree</em>, etc. </p>
  
  <p>The most prominent example of this is the <a href="https://www.cs.toronto.edu/~kriz/cifar.html" rel="noreferrer">cifar</a> dataset which has two versions: a coarse one that has 10 classes and a fine one that has 100 classes, which are all subclasses of the coarse classes. For example a coarse class is <em>fish</em>, while the fine ones are <em>aquarium fish</em>, <em>flatfish</em>, <em>ray</em>, <em>shark</em>, <em>trout</em>, etc.</p>
  
  <p>For semantic segmentation an example could be the following: you want to make a street segmentation model. This a coarse segmentation would mean that it just splits the image into <em>road</em>, <em>vehicle</em>, etc. A fine segmentation, on the other hand, could also detect the type of vehicle, e.g. <em>truck</em>, <em>car</em>, etc.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-07-30T23:02:45.397Z
Answers.LastActivityDate: 2019-07-30T23:02:45.397Z
Answers.UserDisplayName: Djib2011
Body: <p>I was reading the well know paper <a href="https://arxiv.org/pdf/1411.4038.pdf" rel="nofollow noreferrer">Fully Convolutional Networks for Semantic Segmentation</a>, and, throughout the whole paper, they talk use the term <strong>fine</strong> and <strong>coarse</strong>. I was wondering what they mean. The first time they say it in the intro is:</p>
  
  <blockquote>
    <p>Convolutional networks are driving advances in recognition. Convnets are not only improving for whole-image classification, but also making progress on local tasks with structured output. These include advances in
    bounding box object detection, part and keypoint prediction, and local correspondence.</p>
    
    <p>The natural next step in the progression from <strong>coarse</strong> to <strong>fine</strong> inference is to make a prediction at every pixel.</p>
  </blockquote>
  
  <p>It's also used in other parts of the paper</p>
  
  <blockquote>
    <p>We next explain how to convert classification nets into fully convolutional nets that produce <strong>coarse</strong> output maps.</p>
  </blockquote>
  
  <p><em>What do "coarse" and "fine" mean in the context of this paper? And in the general context of computer vision?</em></p>
  
  <p>In English, "coarse" means "rough or loose in texture or grain" , while "fine" means "involving great attention to detail" or "(chiefly of wood) having a fine or delicate arrangement of fibers", but these definitions do not elucidate the meaning of these words in the context of computer vision.</p>
  
  <p>This question was also asked <a href="https://qr.ae/pNKKea" rel="nofollow noreferrer">here</a>.</p>
  
Tags: <convolutional-neural-networks><computer-vision><terminology><image-segmentation><fully-convolutional-networks>
Title: What do the words "coarse" and "fine" mean in the context of computer vision?
UserDisplayName: Charlie Parker
ThreadText: # What do the words "coarse" and "fine" mean in the context of computer vision?
  
  (Asked by: username_0 on 2019-07-29)
  
  I was reading the well know paper [Fully Convolutional Networks for Semantic Segmentation](https://arxiv.org/pdf/1411.4038.pdf), and, throughout the whole paper, they talk use the term **fine** and **coarse**. I was wondering what they mean. The first time they say it in the intro is:
  
  > Convolutional networks are driving advances in recognition. Convnets are not only improving for whole-image classification, but also making progress on local tasks with structured output. These include advances in
  > bounding box object detection, part and keypoint prediction, and local correspondence.
  >
  > The natural next step in the progression from **coarse** to **fine** inference is to make a prediction at every pixel.
  
  It's also used in other parts of the paper
  
  > We next explain how to convert classification nets into fully convolutional nets that produce **coarse** output maps.
  
  *What do "coarse" and "fine" mean in the context of this paper? And in the general context of computer vision?*
  
  In English, "coarse" means "rough or loose in texture or grain" , while "fine" means "involving great attention to detail" or "(chiefly of wood) having a fine or delicate arrangement of fibers", but these definitions do not elucidate the meaning of these words in the context of computer vision.
  
  This question was also asked [here](https://qr.ae/pNKKea).
  
  **Tags:** convolutional-neural-networks, computer-vision, terminology, image-segmentation, fully-convolutional-networks
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-07-30. Score: 7
  
  ### tl;dr
  
  > What does that mean in the context of this paper?
  
  With "coarse segmentation" the author means a segmentation that doesn't have much detail. "Fine segmentation", on the other hand, refers to a segmentation with a high level of detail.
  
  > But also more importantly *[what does that mean in the context of]* general computer vision?
  
  The most common use in CV is to describe how general or specific a class in is classification. A "coarse class" is a very broad one, while a "fine class" is a very specific one.
  
  ### Intended use
  
  What the author refers to is the **level of detail** of the resulting segmentation.
  
  A **coarse** segmentation would mean that we have large blobs covering each class without much detail. On the other hand, a **fine** segmentation would have a much higher level of detail which can even go down to pixel level (i.e. pixel-by-pixel correct segmentation).
  
  To make this clear look at the following two examples. As we go from the left to right, the segmentation maps go from coarse to fine:
  
  ![](https://i.sstatic.net/tamtb.png)
  ![](https://i.sstatic.net/vT8Pe.png)
  
  Note that in the rightmost images the result an almost pixel-perfect (i.e. fine details) segmentation map, while the ones on the left don't have much detail and can be considered coarse.
  
  ### Alternative use
  
  Because this isn't an established terminology, some of the times coarse and fine can refer to the nature of the classes in a classification task. Take the top image for example; the label for a coarse classification task could be a *tree*. For a fine classification task we would have labels like *oak tree*, *pine tree*, etc.
  
  The most prominent example of this is the [cifar](https://www.cs.toronto.edu/~kriz/cifar.html) dataset which has two versions: a coarse one that has 10 classes and a fine one that has 100 classes, which are all subclasses of the coarse classes. For example a coarse class is *fish*, while the fine ones are *aquarium fish*, *flatfish*, *ray*, *shark*, *trout*, etc.
  
  For semantic segmentation an example could be the following: you want to make a street segmentation model. This a coarse segmentation would mean that it just splits the image into *road*, *vehicle*, etc. A fine segmentation, on the other hand, could also detect the type of vehicle, e.g. *truck*, *car*, etc.
  
  
  ---
  
  