Signal Processing - November 2017 - 39
DEEP LEARNING FOR VISUAL UNDERSTANDING
Seunghoon Hong, Suha Kwak, and Bohyung Han
Weakly Supervised Learning with Deep Convolutional
Neural Networks for Semantic Segmentation
Understanding semantic layout of images with minimum human supervision
S
emantic segmentation is a popular visual recognition task
whose goal is to estimate pixel-level object class labels in
images. This problem has been recently handled by deep
convolutional neural networks (DCNNs), and the state-of-theart techniques achieve impressive records on public benchmark
data sets. However, learning DCNNs demand a large number of
annotated training data while segmentation annotations in existing data sets are significantly limited in terms of both quantity
and diversity due to the heavy annotation cost. Weakly supervised approaches tackle this issue by leveraging weak annotations such as image-level labels and bounding boxes, which are
either readily available in existing large-scale data sets for image
classification and object detection or easily obtained thanks to
their low annotation costs. The main challenge in weakly supervised semantic segmentation then is the incomplete annotations
that miss accurate object boundary information required to learn
segmentation. This article provides a comprehensive overview of
weakly supervised approaches for semantic segmentation. Specifically, we describe how the approaches overcome the limitations and discuss research directions worthy of investigation to
improve performance.
Introduction
©ISTOCKPHOTO.COM/ZAPP2PHOTO
Digital Object Identifier 10.1109/MSP.2017.2742558
Date of publication: 13 November 2017
1053-5888/17©2017IEEE
Over the past few years, we observed significant advances in
visual recognition techniques, which are particularly attributed to
the recent development of DCNNs [25]. DCNNs learn a feature
hierarchy directly from raw data, and the learned features are, in
general, richer and more powerful than manually designed ones
that had been widely used before the era of deep learning. Also,
DCNNs can further improve their capacity by optimizing their
decision makers (e.g., classifier) and the feature extractors jointly
in an end-to-end manner. These potentials of DCNNs are realized
recently thanks to the development of novel learning algorithms,
large-scale training data sets, and computer hardware supporting
massively parallel computation. The success story of DCNNs in
visual recognition includes image classifiers surpassing humanlevel performance [14], [15], object detectors meeting both excellent accuracy and real-time speed [30], [41], and other models
IEEE SIGNAL PROCESSING MAGAZINE
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November 2017
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39
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Table of Contents for the Digital Edition of Signal Processing - November 2017
Signal Processing - November 2017 - Cover1
Signal Processing - November 2017 - Cover2
Signal Processing - November 2017 - 1
Signal Processing - November 2017 - 2
Signal Processing - November 2017 - 3
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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