190 research outputs found
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
COMIC: Towards A Compact Image Captioning Model with Attention
Recent works in image captioning have shown very promising raw performance.
However, we realize that most of these encoder-decoder style networks with
attention do not scale naturally to large vocabulary size, making them
difficult to be deployed on embedded system with limited hardware resources.
This is because the size of word and output embedding matrices grow
proportionally with the size of vocabulary, adversely affecting the compactness
of these networks. To address this limitation, this paper introduces a brand
new idea in the domain of image captioning. That is, we tackle the problem of
compactness of image captioning models which is hitherto unexplored. We showed
that, our proposed model, named COMIC for COMpact Image Captioning, achieves
comparable results in five common evaluation metrics with state-of-the-art
approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an
embedding vocabulary size that is 39x - 99x smaller. The source code and models
are available at:
https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-AttentionComment: Added source code link and new results in Table
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