<p>The CRFs are still used for the tasks of image labeling and semantic image segmentation along with the DNNs. In fact, CRFs and DNNs are not self-excluding techniques and a lot of recent publications use both of them.</p>

<p>CRFs are based on probabilistic graphical models, where graph nodes and edges represent random variables, initialized with <em>potential functions</em>. DNN can be used as such potential function:</p>

<ul>
<li><a href="http://www.robots.ox.ac.uk/~tvg/publications/2017/CRFMeetCNN4SemanticSegmentation.pdf" rel="noreferrer">Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation</a></li>
<li><a href="https://www.robots.ox.ac.uk/~szheng/papers/CRFasRNN.pdf" rel="noreferrer">Conditional Random Fields as Recurrent Neural Networks</a></li>
<li><a href="http://www.professeurs.polymtl.ca/christopher.pal/miccai2014/brats2014_lisa_cnn_paper.pdf" rel="noreferrer">Brain Tumor Segmentation with Deep Neural Network</a> (Future Work Section)</li>
</ul>

<p>DCNN may be used for the feature extraction process, which is an essential step in applying CRFs:</p>

<ul>
<li><a href="http://www.project-10.de/Kosov/files/PRJ_2018.pdf" rel="noreferrer">Environmental Microorganism Classification Using Conditional Random Fields and Deep Convolutional Neural Networks</a></li>
<li><a href="https://arxiv.org/pdf/1711.04483.pdf" rel="noreferrer">Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation</a></li>
</ul>

<p>There are also toolkits, combining both CRFs and DNNs:</p>

<ul>
<li><a href="http://research.project-10.de/dgm/" rel="noreferrer">Direct graphical models C++ library</a> </li>
</ul>
conditional random field in semantic segmentation
<p>Are CRF (Conditional Random Fields) still actively used in semantic segmentation tasks or do the current deep neural networks made them unnecessary ? 
I've seen both of the answers in academic papers and, since it seems quite complicated to implement and infer, I would like to have opinions on them before trying them out.</p>

<p>Thank you</p>