IEEE Signal Processing Magazine - January 2018 - 37
DEEP LEARNING FOR VISUAL UNDERSTANDING:
PART 2
Anurag Arnab, Shuai Zheng, Sadeep Jayasumana,
Bernardino Romera-Paredes, Måns Larsson,
Alexander Kirillov, Bogdan Savchynskyy,
Carsten Rother, Fredrik Kahl, and Philip H.S. Torr
Conditional Random Fields Meet
Deep Neural Networks for Semantic Segmentation
Combining probabilistic graphical models with deep learning for structured prediction
S
emantic 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.
Introduction
©ISTOCKPHOTO.COM/ZAPP2PHOTO
Digital Object Identifier 10.1109/MSP.2017.2762355
Date of publication: 9 January 2018
1053-5888/18©2018IEEE
Scene understanding is a long-standing problem in the field of
computer vision and involves developing algorithms to interpret the contents of images with the level of comprehension of
a human. Perceptual tasks, such as visual scene understanding,
are performed effortlessly by humans. However, replicating
the visual cortex on a computer has proven to be a challenging problem that has yet to be completely solved, almost 50
years after it was first posed by an undergraduate student at the
Massachusetts Institute of Technology as a summer research
project [1].
There are many ways of describing a scene, and current
computer vision research addresses most of these problems
independently, as illustrated in Figure 1. A high-level summary of a scene can be obtained by predicting image tags that
describe the objects in the picture (such as person) or the scene
(such as city or office). This task is known as image classification. The object detection task, on the other hand, aims
IEEE SIGNAL PROCESSING MAGAZINE
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January 2018
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37
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Table of Contents for the Digital Edition of IEEE Signal Processing Magazine - January 2018
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