<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>

CC BY-SA 4.0
2020-10-18T08:57:12.403Z
2020-10-18T08:57:12.403Z
Saurav Maheshkar
<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>

<computer-vision><state-of-the-art><conditional-random-field>
Are Markov Random Fields and Conditional Random Fields still used in computer vision?
FourierFlux
# 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


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## 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)


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