Signal Processing - November 2017 - 109
DEEP LEARNING FOR VISUAL UNDERSTANDING
Xiaodong He and Li Deng
Deep Learning for Image-to-Text Generation
A technical overview
G
enerating a natural language description from an image is
an emerging interdisciplinary problem at the intersection of
computer vision, natural language processing, and artificial
intelligence (AI). This task, often referred to as image or visual
captioning, forms the technical foundation of many important
applications, such as semantic visual search, visual intelligence
in chatting robots, photo and video sharing in social media, and
aid for visually impaired people to perceive surrounding visual
content. Thanks to the recent advances in deep learning, the AI
research community has witnessed tremendous progress in visual captioning in recent years. In this article, we will first summarize this exciting emerging visual captioning area. We will
then analyze the key development and the major progress the
community has made, their impact in both research and industry
deployment, and what lies ahead in future breakthroughs.
Introduction
©ISTOCKPHOTO.COM/ZAPP2PHOTO
Digital Object Identifier 10.1109/MSP.2017.2741510
Date of publication: 13 November 2017
1053-5888/17©2017IEEE
It has been long envisioned that machines one day will understand the visual world at a human level of intelligence. Thanks
to the progress in deep learning [15], [36], [59], [60], [69],
researchers can now build very deep convolutional neural
networks (CNNs) and achieve an impressively low error rate
for tasks like large-scale image classification [9], [15], [23]. In
these tasks, one way for researchers to train a model to predict
the category of a given image is to first annotate each image in
a training set with a label from a predefined set of categories.
Through such fully supervised training, the computer learns
how to classify an image.
However, in tasks like image classification, the content of an
image is usually simple, containing a predominant object to be
classified. The situation could be much more challenging when
we want computers to understand complex scenes. Image captioning is one such task. The challenges come from two perspectives.
First, to generate a semantically meaningful and syntactically
fluent caption, the system needs to detect salient semantic concepts in the image, understand the relationships among them, and
compose a coherent description about the overall content of the
image, which involves language and common-sense knowledge
IEEE SIGNAL PROCESSING MAGAZINE
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November 2017
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109
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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
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Signal Processing - November 2017 - Cover3
Signal Processing - November 2017 - Cover4
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