Answers.Body: <p>In the <a href="https://arxiv.org/abs/1611.07004" rel="nofollow noreferrer"><strong>Image-to-Image Translation with Conditional Adversarial Networks</strong></a> paper (popularly known as <em>pix2pix</em>), they used a Markovian Discriminator to effectively model the image as a Markovian Random Field.</p>
  <p>There were some papers in the last 5 years concerning Markov Fields. Here are some of them:-</p>
  <ol>
  <li><p><a href="https://arxiv.org/abs/1601.04589" rel="nofollow noreferrer">Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis</a></p>
  </li>
  <li><p><a href="https://arxiv.org/abs/1604.04382" rel="nofollow noreferrer">Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks</a></p>
  </li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-10-18T08:57:12.403Z
Answers.LastActivityDate: 2020-10-18T08:57:12.403Z
Answers.UserDisplayName: Saurav Maheshkar
Body: <p>Back before deep learning, there were a lot of different attempts at computer vision. Some involved Conditional Random Fields and Markov Random Fields, which were both computationally difficult and hard to understand/implement.</p>
  <p>Are these areas still being developed in the computer vision domain? What was the end result of this line of study? I haven't seen any papers on this topic be cited in top-performing benchmarks, so I assume nobody cares about them anymore, but I wanted to ask.</p>
  
Tags: <computer-vision><state-of-the-art><conditional-random-field>
Title: Are Markov Random Fields and Conditional Random Fields still used in computer vision?
UserDisplayName: FourierFlux
ThreadText: # Are Markov Random Fields and Conditional Random Fields still used in computer vision?
  
  (Asked by: username_0 on 2020-10-15)
  
  Back before deep learning, there were a lot of different attempts at computer vision. Some involved Conditional Random Fields and Markov Random Fields, which were both computationally difficult and hard to understand/implement.
  
  Are these areas still being developed in the computer vision domain? What was the end result of this line of study? I haven't seen any papers on this topic be cited in top-performing benchmarks, so I assume nobody cares about them anymore, but I wanted to ask.
  
  **Tags:** computer-vision, state-of-the-art, conditional-random-field
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-10-18. Score: 1
  
  In the [**Image-to-Image Translation with Conditional Adversarial Networks**](https://arxiv.org/abs/1611.07004) paper (popularly known as *pix2pix*), they used a Markovian Discriminator to effectively model the image as a Markovian Random Field.
  
  There were some papers in the last 5 years concerning Markov Fields. Here are some of them:-
  
  1. [Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis](https://arxiv.org/abs/1601.04589)
  2. [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks](https://arxiv.org/abs/1604.04382)
  
  
  ---
  
  