Video Understanding Using Multimodal Deep Learning
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Video Understanding Using Multimodal Deep Learning
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Author : Arsha Nagrani
language : en
Publisher:
Release Date : 2020
Video Understanding Using Multimodal Deep Learning written by Arsha Nagrani and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Multimodal Scene Understanding
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Author : Michael Yang
language : en
Publisher: Academic Press
Release Date : 2019-07-16
Multimodal Scene Understanding written by Michael Yang and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-16 with Computers categories.
Multimodal Scene Understanding: Algorithms, Applications and Deep Learning presents recent advances in multi-modal computing, with a focus on computer vision and photogrammetry. It provides the latest algorithms and applications that involve combining multiple sources of information and describes the role and approaches of multi-sensory data and multi-modal deep learning. The book is ideal for researchers from the fields of computer vision, remote sensing, robotics, and photogrammetry, thus helping foster interdisciplinary interaction and collaboration between these realms. Researchers collecting and analyzing multi-sensory data collections – for example, KITTI benchmark (stereo+laser) - from different platforms, such as autonomous vehicles, surveillance cameras, UAVs, planes and satellites will find this book to be very useful. Contains state-of-the-art developments on multi-modal computing Shines a focus on algorithms and applications Presents novel deep learning topics on multi-sensor fusion and multi-modal deep learning
Multimodal Learning Toward Micro Video Understanding
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Author : Liqiang Nie
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Multimodal Learning Toward Micro Video Understanding written by Liqiang Nie and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
Micro-videos, a new form of user-generated contents, have been spreading widely across various social platforms, such as Vine, Kuaishou, and Tik Tok. Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to its brevity and low bandwidth cost, micro-videos are gaining increasing user enthusiasm. The blossoming of micro-videos opens the door to the possibility of many promising applications, ranging from network content caching to online advertising. Thus, it is highly desirable to develop an effective scheme for the high-order micro-video understanding. Micro-video understanding is, however, non-trivial due to the following challenges: (1) how to represent micro-videos that only convey one or few high-level themes or concepts; (2) how to utilize the hierarchical structure of the venue categories to guide the micro-video analysis; (3) how to alleviate the influence of low-quality caused by complex surrounding environments and the camera shake; (4) how to model the multimodal sequential data, {i.e.}, textual, acoustic, visual, and social modalities, to enhance the micro-video understanding; and (5) how to construct large-scale benchmark datasets for the analysis? These challenges have been largely unexplored to date. In this book, we focus on addressing the challenges presented above by proposing some state-of-the-art multimodal learning theories. To demonstrate the effectiveness of these models, we apply them to three practical tasks of micro-video understanding: popularity prediction, venue category estimation, and micro-video routing. Particularly, we first build three large-scale real-world micro-video datasets for these practical tasks. We then present a multimodal transductive learning framework for micro-video popularity prediction. Furthermore, we introduce several multimodal cooperative learning approaches and a multimodal transfer learning scheme for micro-video venue category estimation. Meanwhile, we develop a multimodal sequential learning approach for micro-video recommendation. Finally, we conclude the book and figure out the future research directions in multimodal learning toward micro-video understanding.
Multi Modal Deep Learning To Understand Vision And Language
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Author : Shagan Sah
language : en
Publisher:
Release Date : 2018
Multi Modal Deep Learning To Understand Vision And Language written by Shagan Sah and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Computer vision categories.
"Developing intelligent agents that can perceive and understand the rich visual world around us has been a long-standing goal in the field of artificial intelligence. In the last few years, significant progress has been made towards this goal and deep learning has been attributed to recent incredible advances in general visual and language understanding. Convolutional neural networks have been used to learn image representations while recurrent neural networks have demonstrated the ability to generate text from visual stimuli. In this thesis, we develop methods and techniques using hybrid convolutional and recurrent neural network architectures that connect visual data and natural language utterances. Towards appreciating these methods, this work is divided into two broad groups. Firstly, we introduce a general purpose attention mechanism modeled using a continuous function for video understanding. The use of an attention based hierarchical approach along with automatic boundary detection advances state-of-the-art video captioning results. We also develop techniques for summarizing and annotating long videos. In the second part, we introduce architectures along with training techniques to produce a common connection space where natural language sentences are efficiently and accurately connected with visual modalities. In this connection space, similar concepts lie close, while dissimilar concepts lie far apart, irrespective` of their modality. We discuss four modality transformations: visual to text, text to visual, visual to visual and text to text. We introduce a novel attention mechanism to align multi-modal embeddings which are learned through a multi-modal metric loss function. The common vector space is shown to enable bidirectional generation of images and text. The learned common vector space is evaluated on multiple image-text datasets for cross-modal retrieval and zero-shot retrieval. The models are shown to advance the state-of-the-art on tasks that require joint processing of images and natural language."--Abstract.
Deep Learning For Video Understanding
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Author : Zuxuan Wu
language : en
Publisher: Springer Nature
Release Date :
Deep Learning For Video Understanding written by Zuxuan Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Multimodal Deep Learning Methods For Person Annotation In Video Sequences
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Author : David Rodríguez Navarro
language : en
Publisher:
Release Date : 2017
Multimodal Deep Learning Methods For Person Annotation In Video Sequences written by David Rodríguez Navarro and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
In unsupervised identity recognition in video sequences systems, which is a very active field of research in computer vision, the use of convolutional neural networks (CNN's) is currently gaining a lot of interest due to the great results that this techniques have been shown in face recognition and verification problems in recent years. In this thesis, the improvement of a CNN applied for face verification will be made in the context of an unsupervised identity annotation system developed for the MediaEval 2016 task. This improvement will be achieved by training the 2016 CNN architecture with images from the task database, which is now possible since we can use the last version outputs, along with a data augmentation method applied to the previously extracted samples. In addition, a new multimodal verification system is implemented merging both visual and audio feature vectors. An evaluation of the margin of improvement that these techniques introduce in the whole system will be made, comparing against the State-of-the-Art. Finally some conclusions will be exposed based on the obtained results will be drawn along with some possible future lines of work.
Multimodal Video Characterization And Summarization
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Author : Michael A. Smith
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-17
Multimodal Video Characterization And Summarization written by Michael A. Smith and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-17 with Computers categories.
Multimodal Video Characterization and Summarization is a valuable research tool for both professionals and academicians working in the video field. This book describes the methodology for using multimodal audio, image, and text technology to characterize video content. This new and groundbreaking science has led to many advances in video understanding, such as the development of a video summary. Applications and methodology for creating video summaries are described, as well as user-studies for evaluation and testing.
Multimodal Behavior Analysis In The Wild
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Author : Xavier Alameda-Pineda
language : en
Publisher: Academic Press
Release Date : 2018-11-13
Multimodal Behavior Analysis In The Wild written by Xavier Alameda-Pineda and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Computers categories.
Multimodal Behavioral Analysis in the Wild: Advances and Challenges presents the state-of- the-art in behavioral signal processing using different data modalities, with a special focus on identifying the strengths and limitations of current technologies. The book focuses on audio and video modalities, while also emphasizing emerging modalities, such as accelerometer or proximity data. It covers tasks at different levels of complexity, from low level (speaker detection, sensorimotor links, source separation), through middle level (conversational group detection, addresser and addressee identification), and high level (personality and emotion recognition), providing insights on how to exploit inter-level and intra-level links. This is a valuable resource on the state-of-the- art and future research challenges of multi-modal behavioral analysis in the wild. It is suitable for researchers and graduate students in the fields of computer vision, audio processing, pattern recognition, machine learning and social signal processing. Gives a comprehensive collection of information on the state-of-the-art, limitations, and challenges associated with extracting behavioral cues from real-world scenarios Presents numerous applications on how different behavioral cues have been successfully extracted from different data sources Provides a wide variety of methodologies used to extract behavioral cues from multi-modal data
Learning Video Representation From Self Supervision
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Author : Brian Chen
language : en
Publisher:
Release Date : 2023
Learning Video Representation From Self Supervision written by Brian Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
This thesis investigates the problem of learning video representations for video understanding. Previous works have explored the use of data-driven deep learning approaches, which have been shown to be effective in learning useful video representations. However, obtaining large amounts of labeled data can be costly and time-consuming. We investigate self-supervised approach as for multimodal video data to overcome this challenge. Video data typically contains multiple modalities, such as visual, audio, transcribed speech, and textual captions, which can serve as pseudo-labels for representation learning without needing manual labeling. By utilizing these modalities, we can train deep representations over large-scale video data consisting of millions of video clips collected from the internet. We demonstrate the scalability benefits of multimodal self-supervision by achieving new state-of-the-art performance in various domains, including video action recognition, text-to-video retrieval, and text-to-video grounding.
Ireland S Cause In England S Parliament
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Author : Justin McCarthy
language : en
Publisher:
Release Date : 1887
Ireland S Cause In England S Parliament written by Justin McCarthy and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1887 with Home rule (Ireland) categories.