Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation: Combining Probabilistic Graphical Models with Deep Learning for Structured Prediction
Artikel i vetenskaplig tidskrift, 2018

Semantic segmentation is the task of labeling every pixel in an image with a predefined object category. It has numerous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as conditional random fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, deep neural networks (DNNs) recently have been shown to excel at a wide range of computer vision problems due to their ability to automatically learn rich feature representations from data, as opposed to traditional handcrafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modeling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarize future research directions.

Image segmentation


Computer vision


Computational modeling

Feature extraction


Anurag Arnab

Unknown organization

Shuai Zheng

Unknown organization

Sadeep Jayasumana

Unknown organization

Bernardino Romera-Paredes

Unknown organization

Måns Larsson

Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Alexander Kirillov

Unknown organization

Bogdan Savchynskyy

Unknown organization

Carsten Rother

Unknown organization

Fredrik Kahl

Elektroteknik, Signalbehandling och medicinsk teknik, Digitala bildsystem och bildanalys

Philip H.S. Torr

Unknown organization

IEEE Signal Processing Magazine

1053-5888 (ISSN)

Vol. 35 37-52


Informations- och kommunikationsteknik


Datorseende och robotik (autonoma system)