Wenwu Zhu – författare

While most books on the subject focus on resource allocation in just one type of network, this book is the first to examine the common characteristics of multiple distributed video communication systems. Comprehensive and systematic, Optimal Resource Allocation for Distributed Video Communication presents a unified optimization framework for resource allocation across these systems. The book examines the techniques required for optimal resource allocation over Internet, wireless cellular networks, wireless ad hoc networks, and wireless sensor networks. It provides you with the required foundation in video communications, including Peer-to-Peer (P2P) networks, wireless networks, and visual sensor networks. Whether you’re in industry or academia, you’ll value how the book outlines current challenges facing the field and outlines a general solution framework for addressing these challenges. From problem formulations and theoretical analysis to practical algorithms, it facilitates the comprehensive understanding required to achieve optimized video and multimedia communications.Presents the resource allocation techniques for scalable video communications over Internet or wireless networksExamines two resource allocation problems—distributed throughput maximization for scalable P2P Video-on-Demand (VoD) systems and streaming capacity for P2P VoD systemsOutlines an optimal prefetching framework for reducing seeking delays in P2P VoD applicationsExamines distributed optimization techniques for unicast and multicast video streaming over wireless ad hoc networksConsiders the network lifetime maximization problem in wireless visual sensor networks Detailing methods that can immediately improve the performance of your video communication systems, this book presents multiple applications of optimal resource allocation. For each of the applications,

While most books on the subject focus on resource allocation in just one type of network, this book is the first to examine the common characteristics of multiple distributed video communication systems. Comprehensive and systematic, Optimal Resource Allocation for Distributed Video Communication presents a unified optimization framework for resource allocation across these systems. The book examines the techniques required for optimal resource allocation over Internet, wireless cellular networks, wireless ad hoc networks, and wireless sensor networks. It provides you with the required foundation in video communications, including Peer-to-Peer (P2P) networks, wireless networks, and visual sensor networks. Whether you’re in industry or academia, you’ll value how the book outlines current challenges facing the field and outlines a general solution framework for addressing these challenges. From problem formulations and theoretical analysis to practical algorithms, it facilitates the comprehensive understanding required to achieve optimized video and multimedia communications.Presents the resource allocation techniques for scalable video communications over Internet or wireless networksExamines two resource allocation problems—distributed throughput maximization for scalable P2P Video-on-Demand (VoD) systems and streaming capacity for P2P VoD systemsOutlines an optimal prefetching framework for reducing seeking delays in P2P VoD applicationsExamines distributed optimization techniques for unicast and multicast video streaming over wireless ad hoc networksConsiders the network lifetime maximization problem in wireless visual sensor networks Detailing methods that can immediately improve the performance of your video communication systems, this book presents multiple applications of optimal resource allocation. For each of the applications,

This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields.

This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields.

This book consists of two parts: Part I Methodologies for Video Grounding and Part II Generalized Video Grounding and Trending Directions. To make this book self-contained and cutting edge, Part I will cover basic and advanced methodologies for Video Grounding, discussing key comparisons with several representative Vision-Language learning tasks including multimodal understanding and generation. Part II will cover our insights for Generalized Video Grounding and the development of Video Grounding in the era of large foundation models, discussing future directions such as Out-of-Distribution settings which deserve further investigations.Discussions on Video Grounding will cover both the task of Video Grounding and other Vision-Language Task, as well as their relations. The basics and advances will touch Video Grounding from model to benchmark, from supervised learning to unsupervised pre-training, from single video grounding to video corpus grounding, and from in-distribution setting to out-of-distribution setting. As for Generalized Video Grounding, we discuss cross-modal grounding, event grounding for multi-modal tasks, various distribution shifts in out-of-distribution setting, explainable Video Grounding, and large foundation model for Video Grounding.We deeply hope this book can benefit interested readers from both academy and industry, covering needs from junior starters in research to senior practitioners in IT companies.

This bookconstitutes the proceedings of the 7th International Conference on SocialInformatics, SocInfo 2015, held in Beijing, China, in December 2015. The 19papers presented in this volume were carefully reviewed and selected from 42submissions. They cover topics such as user modeling, opinion mining, userbehavior, and crowd sourcing.

This brief presents new architecture and strategies for distribution of social video content. A primary framework for socially-aware video delivery and a thorough overview of the possible approaches is provided. The book identifies the unique characteristics of socially-aware video access and social content propagation, revealing the design and integration of individual modules that are aimed at enhancing user experience in the social network context. The change in video content generation, propagation, and consumption for online social networks, has significantly challenged the traditional video delivery paradigm. Given the massive amount of user-generated content shared in online social networks, users are now engaged as active participants in the social ecosystem rather than as passive receivers of media content. This revolution is being driven further by the deep penetration of 3G/4G wireless networks and smart mobile devices that are seamlessly integrated with online social networking and media-sharing services. Despite increasingly abundant bandwidth and computational resources, the ever-increasing volume of data created by user-generated video content--along with the boundless coverage of socialized sharing--presents unprecedented challenges.

This book explains how to use a data-driven approach to design strategies for social media content delivery. It first introduces readers to how social information can be effectively gathered for big data analysis, which provides content delivery intelligence. Secondly, the book describes data-driven models to capture information diffusion in online social networks and social media content propagation and popularity, before presenting prediction models for social media content delivery. By addressing the resource allocation and content replication aspects of social media content delivery, the book presents the latest data-driven strategies. In closing, it outlines a number of potential research directions regarding social media content delivery.

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output.

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output.