19,722 research outputs found
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Question categorization and expert retrieval methods have been crucial for
information organization and accessibility in community question & answering
(CQA) platforms. Research in this area, however, has dealt with only the text
modality. With the increasing multimodal nature of web content, we focus on
extending these methods for CQA questions accompanied by images. Specifically,
we leverage the success of representation learning for text and images in the
visual question answering (VQA) domain, and adapt the underlying concept and
architecture for automated category classification and expert retrieval on
image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of
Yahoo! Answers.
To the best of our knowledge, this is the first work to tackle the
multimodality challenge in CQA, and to adapt VQA models for tasks on a more
ecologically valid source of visual questions. Our analysis of the differences
between visual QA and community QA data drives our proposal of novel
augmentations of an attention method tailored for CQA, and use of auxiliary
tasks for learning better grounding features. Our final model markedly
outperforms the text-only and VQA model baselines for both tasks of
classification and expert retrieval on real-world multimodal CQA data.Comment: Submitted for review at CIKM 201
Strategies for Searching Video Content with Text Queries or Video Examples
The large number of user-generated videos uploaded on to the Internet
everyday has led to many commercial video search engines, which mainly rely on
text metadata for search. However, metadata is often lacking for user-generated
videos, thus these videos are unsearchable by current search engines.
Therefore, content-based video retrieval (CBVR) tackles this metadata-scarcity
problem by directly analyzing the visual and audio streams of each video. CBVR
encompasses multiple research topics, including low-level feature design,
feature fusion, semantic detector training and video search/reranking. We
present novel strategies in these topics to enhance CBVR in both accuracy and
speed under different query inputs, including pure textual queries and query by
video examples. Our proposed strategies have been incorporated into our
submission for the TRECVID 2014 Multimedia Event Detection evaluation, where
our system outperformed other submissions in both text queries and video
example queries, thus demonstrating the effectiveness of our proposed
approaches
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Parameterized Neural Network Language Models for Information Retrieval
Information Retrieval (IR) models need to deal with two difficult issues,
vocabulary mismatch and term dependencies. Vocabulary mismatch corresponds to
the difficulty of retrieving relevant documents that do not contain exact query
terms but semantically related terms. Term dependencies refers to the need of
considering the relationship between the words of the query when estimating the
relevance of a document. A multitude of solutions has been proposed to solve
each of these two problems, but no principled model solve both. In parallel, in
the last few years, language models based on neural networks have been used to
cope with complex natural language processing tasks like emotion and paraphrase
detection. Although they present good abilities to cope with both term
dependencies and vocabulary mismatch problems, thanks to the distributed
representation of words they are based upon, such models could not be used
readily in IR, where the estimation of one language model per document (or
query) is required. This is both computationally unfeasible and prone to
over-fitting. Based on a recent work that proposed to learn a generic language
model that can be modified through a set of document-specific parameters, we
explore use of new neural network models that are adapted to ad-hoc IR tasks.
Within the language model IR framework, we propose and study the use of a
generic language model as well as a document-specific language model. Both can
be used as a smoothing component, but the latter is more adapted to the
document at hand and has the potential of being used as a full document
language model. We experiment with such models and analyze their results on
TREC-1 to 8 datasets
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
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