Efficient Likelihood Learning of a Generic CNN-CRF Model for Semantic Segmentation

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Recent Trends in Deep Learning Based Natural Language Processing (2017, neutral): “In such cases, structured prediction techniques such as conditional random field (CRF) are sometimes employed to better capture dependencies between adjacent class labels and finally generate cohesive label sequence giving maximum score to the whole sentence (Kirillov et al., 2015).”
Pixel-Level Encoding and Depth Layering for Instance-Level Semantic Labeling (2016, background): “Pixel-level semantic labeling based on neural networks has been very successful [4,17,21,33,36].”
Fast Semantic Image Segmentation with High Order Context and Guided Filtering (2016, methodology): “Traditionally, the derivation of partition function can also be computed by Gibbs sampling [14].”
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation (2017, methodology): “In [52], a generic CNN-CRF model was proposed to handle arbitrary potentials for labeling body parts in depth images.”
Recent Trends in Deep Learning Based Natural Language Processing [Review Article] (2018, background): “labels and finally generate cohesive label sequence giving maximum score to the whole sentence [46].”