The Temporal Structure of Multimodal Communication, Laszlo Hunyadi; Istv?n Szekr?nyes
Автор: Francesca D`Errico; Isabella Poggi; Alessandro Vin Название: Conflict and Multimodal Communication ISBN: 3319140809 ISBN-13(EAN): 9783319140803 Издательство: Springer Рейтинг: Цена: 19564.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Conflict and Multimodal Communication
Автор: Angel D. Sappa; Jordi Vitri? Название: Multimodal Interaction in Image and Video Applications ISBN: 3642439837 ISBN-13(EAN): 9783642439834 Издательство: Springer Рейтинг: Цена: 16977.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows case studies of multimodal interactive technologies for both image and video applications. They cover a wide spectrum of applications, ranging from interactive handwriting transcriptions to human-robot interactions in real environments.
Описание: This edited book will serve as a source of reference for technologies and applications for multimodality data analytics in big data environments. After an introduction, the editors organize the book into four main parts on sentiment, affect and emotion analytics for big multimodal data; unsupervised learning strategies for big multimodal data; supervised learning strategies for big multimodal data; and multimodal big data processing and applications. The book will be of value to researchers, professionals and students in engineering and computer science, particularly those engaged with image and speech processing, multimodal information processing, data science, and artificial intelligence.
Автор: Remley, Dirk Название: Neuroscience of multimodal persuasive messages ISBN: 1138635812 ISBN-13(EAN): 9781138635814 Издательство: Taylor&Francis Рейтинг: Цена: 25265.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this book, Dirk Remley applies his model of integrating multimodal rhetorical theory and multi-sensory neural processing theory pertaining to cognition and learning to multimodal persuasive messages.
Автор: Harry Bunt; Robbert-Jan Beun; Tijn Borghuis Название: Multimodal Human-Computer Communication ISBN: 354064380X ISBN-13(EAN): 9783540643807 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Taken from the First International Conference on Cooperative Multimodal Communication held in Eindhoven, the Netherlands, in 1995, this text addresses such issues as intelligent multimedia retrieval, cooperative conversation, agent system communication and multimodal maps.
Автор: Julian Fierrez; Javier Ortega-Garcia; Anna Esposit Название: Biometric ID Management and Multimodal Communication ISBN: 3642043909 ISBN-13(EAN): 9783642043901 Издательство: Springer Рейтинг: Цена: 10480.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the research papers presented at the Joint 2101 & 2102 International Conference on Biometric ID Management and Multimodal Communication. COST 2101 Action is focused on `Biometrics for Identity Documents and Smart Cards (BIDS)`, while COST 2102 Action is entitled `Cross-Modal Analysis of Verbal and Non-verbal Communication`.
Автор: Harry Bunt; Robbert-Jan Beun Название: Cooperative Multimodal Communication ISBN: 3540428062 ISBN-13(EAN): 9783540428060 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This work constitutes the refereed post-proceedings of the Second International Conference on Cooperative Multimodal Communication, 1998. It covers topical sections on multimodal generation, multimodal co-operation, multimodal interpretation, and multimedia platforms and test environments.
Автор: Nie Liqiang, Liu Meng, Song Xuemeng Название: Multimodal Learning toward Micro-Video Understanding ISBN: 1681736284 ISBN-13(EAN): 9781681736280 Издательство: Mare Nostrum (Eurospan) Цена: 12335.00 р. Наличие на складе: Нет в наличии.
Описание:
Micro-videos, a new form of user-generated content, have been spreading widely across various social platforms, such as Vine, Kuaishou, and TikTok.
Different from traditional long videos, micro-videos are usually recorded by smart mobile devices at any place within a few seconds. Due to their 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 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 venue categories to guide micro-video analysis; (3) how to alleviate the influence of low quality caused by complex surrounding environments and camera shake; (4) how to model multimodal sequential data, i.e. textual, acoustic, visual, and social modalities to enhance micro-video understanding; and (5) how to construct large-scale benchmark datasets for 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.
Описание: This book presents state-of-the-art computationalattention models that have been successfully tested in diverse applicationareas and can build the foundation for artificial systems to efficientlyexplore, analyze, and understand natural scenes.
Автор: Elisabeth Andr?; Laila Dybkj?r; Heiko Neumann; Rob Название: Perception in Multimodal Dialogue Systems ISBN: 3540693688 ISBN-13(EAN): 9783540693680 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The papers are organized in topical sections on multimodal and spoken dialogue systems, classification of dialogue acts and sound, recognition of eye gaze, head poses, mimics and speech as well as combinations of modalities, vocal emotion recognition, human-like and social dialogue systems, and evaluation methods for multimodal dialogue systems.
Описание: This book constitutes the thoroughly refereed post-workshop proceedings of the First IAPR TC3 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS2012), held in Tsukuba, Japan in November 2012, in collaboration with the NLGD Festival of Games.
Second International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2019).- Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification.- UBS: A Dimension-Agnostic Metric for Concept Vector Interpretability Applied to Radiomics.- Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis.- Incorporating Task-Specific Structural Knowledge into CNNs for Brain Midline Shift Detection.- Guideline-based Additive Explanation for Computer-Aided Diagnosis of Lung Nodules.- Deep neural network or dermatologist?.- Towards Interpretability of Segmentation Networks by analyzing DeepDreams.- 9th International Workshop on Multimodal Learning for Clinical Decision Support (ML-CDS 2019).- Towards Automatic Diagnosis from Multi-modal Medical Data.- Deep Learning based Multi-Modal Registration for Retinal Imaging.- Automated Enriched Medical Concept Generation for Chest X-ray Images.
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