3,647 research outputs found
Movie Description
Audio Description (AD) provides linguistic descriptions of movies and allows
visually impaired people to follow a movie along with their peers. Such
descriptions are by design mainly visual and thus naturally form an interesting
data source for computer vision and computational linguistics. In this work we
propose a novel dataset which contains transcribed ADs, which are temporally
aligned to full length movies. In addition we also collected and aligned movie
scripts used in prior work and compare the two sources of descriptions. In
total the Large Scale Movie Description Challenge (LSMDC) contains a parallel
corpus of 118,114 sentences and video clips from 202 movies. First we
characterize the dataset by benchmarking different approaches for generating
video descriptions. Comparing ADs to scripts, we find that ADs are indeed more
visual and describe precisely what is shown rather than what should happen
according to the scripts created prior to movie production. Furthermore, we
present and compare the results of several teams who participated in a
challenge organized in the context of the workshop "Describing and
Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at
ICCV 2015
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Multimodal Data Analytics and Fusion for Data Science
Advances in technologies have rapidly accumulated a zettabyte of “new” data every two years. The huge amount of data have a powerful impact on various areas in science and engineering and generates enormous research opportunities, which calls for the design and development of advanced approaches in data analytics. Given such demands, data science has become an emerging hot topic in both industry and academia, ranging from basic business solutions, technological innovations, and multidisciplinary research to political decisions, urban planning, and policymaking. Within the scope of this dissertation, a multimodal data analytics and fusion framework is proposed for data-driven knowledge discovery and cross-modality semantic concept detection. The proposed framework can explore useful knowledge hidden in different formats of data and incorporate representation learning from data in multimodalities, especial for disaster information management. First, a Feature Affinity-based Multiple Correspondence Analysis (FA-MCA) method is presented to analyze the correlations between low-level features from different features, and an MCA-based Neural Network (MCA-NN) ispro- posedto capture the high-level features from individual FA-MCA models and seamlessly integrate the semantic data representations for video concept detection. Next, a genetic algorithm-based approach is presented for deep neural network selection. Furthermore, the improved genetic algorithm is integrated with deep neural networks to generate populations for producing optimal deep representation learning models. Then, the multimodal deep representation learning framework is proposed to incorporate the semantic representations from data in multiple modalities efficiently. At last, fusion strategies are applied to accommodate multiple modalities. In this framework, cross-modal mapping strategies are also proposed to organize the features in a better structure to improve the overall performance
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
Emotion Recognition by Video: A review
Video emotion recognition is an important branch of affective computing, and
its solutions can be applied in different fields such as human-computer
interaction (HCI) and intelligent medical treatment. Although the number of
papers published in the field of emotion recognition is increasing, there are
few comprehensive literature reviews covering related research on video emotion
recognition. Therefore, this paper selects articles published from 2015 to 2023
to systematize the existing trends in video emotion recognition in related
studies. In this paper, we first talk about two typical emotion models, then we
talk about databases that are frequently utilized for video emotion
recognition, including unimodal databases and multimodal databases. Next, we
look at and classify the specific structure and performance of modern unimodal
and multimodal video emotion recognition methods, talk about the benefits and
drawbacks of each, and then we compare them in detail in the tables. Further,
we sum up the primary difficulties right now looked by video emotion
recognition undertakings and point out probably the most encouraging future
headings, such as establishing an open benchmark database and better multimodal
fusion strategys. The essential objective of this paper is to assist scholarly
and modern scientists with keeping up to date with the most recent advances and
new improvements in this speedy, high-influence field of video emotion
recognition
Egocentric Vision-based Action Recognition: A survey
[EN] The egocentric action recognition EAR field has recently increased its popularity due to the affordable and lightweight wearable cameras available nowadays such as GoPro and similars. Therefore, the amount of egocentric data generated has increased, triggering the interest in the understanding of egocentric videos. More specifically, the recognition of actions in egocentric videos has gained popularity due to the challenge that it poses: the wild movement of the camera and the lack of context make it hard to recognise actions with a performance similar to that of third-person vision solutions. This has ignited the research interest on the field and, nowadays, many public datasets and competitions can be found in both the machine learning and the computer vision communities. In this survey, we aim to analyse the literature on egocentric vision methods and algorithms. For that, we propose a taxonomy to divide the literature into various categories with subcategories, contributing a more fine-grained classification of the available methods. We also provide a review of the zero-shot approaches used by the EAR community, a methodology that could help to transfer EAR algorithms to real-world applications. Finally, we summarise the datasets used by researchers in the literature.We gratefully acknowledge the support of the Basque Govern-ment's Department of Education for the predoctoral funding of the first author. This work has been supported by the Spanish Government under the FuturAAL-Context project (RTI2018-101045-B-C21) and by the Basque Government under the Deustek project (IT-1078-16-D)
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