Cross Modal Learning Adaptivity Prediction And Interaction
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Cross-Modal Learning: Adaptivity, Prediction and Interaction
The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.
The Handbook of Usage-Based Linguistics
Author: Manuel Diaz-Campos
language: en
Publisher: John Wiley & Sons
Release Date: 2023-07-05
The Handbook of Usage-Based Linguistics The Handbook of Usage-Based Linguistics is the first edited volume to provide a comprehensive, authoritative, and interdisciplinary view of usage-based theory in linguistics. Contributions by an international team of established and emerging scholars discuss the application of used-based approaches in phonology, morphosyntax, psycholinguistics, language variation and change, language development, cognitive linguistics, and other subfields of linguistics. Unprecedented in depth and scope, this groundbreaking work of scholarship addresses all major theoretical and methodological aspects of usage-based linguistics while offering diverse perspectives and key insights into theory, history, and methodology. Throughout the text, in-depth essays explore up-to-date methodologies, emerging approaches, new technologies, and cutting-edge research in usage-based linguistics in many languages and subdisciplines. Topics include used-based approaches to subfields such as anthropological linguistics, computational linguistics, statistical analysis, and corpus linguistics. Covering the conceptual foundations, historical development, and future directions of usage-based theory, The Handbook of Usage-Based Linguistics is a must-have reference work for advanced students and scholars in anthropological linguistics, psycholinguistics, cognitive linguistics, corpora analysis, and other subfields of linguistics.
Intelligent and Efficient Video Moment Localization
This book provides a comprehensive exploration of video moment localization, a rapidly emerging research field focused on enabling precise retrieval of specific moments within untrimmed, unsegmented videos. With the rapid growth of digital content and the rise of video-sharing platforms, users face significant challenges when searching for particular content across vast video archives. This book addresses how video moment localization uses natural language queries to bridge the gap between video content and semantic understanding, offering an intuitive solution for locating specific moments across diverse domains like surveillance, education, and entertainment. This book explores the latest advancements in video moment localization, addressing key issues such as accuracy, efficiency, and scalability. It presents innovative techniques for contextual understanding and cross-modal semantic alignment, including attention mechanisms and dynamic query decomposition. Additionally, the book discusses solutions for enhancing computational efficiency and scalability, such as semantic pruning and efficient hashing, while introducing frameworks for better integration between visual and textual data. It also examines weakly-supervised learning approaches to reduce annotation costs without sacrificing performance. Finally, the book covers real-world applications and offers insights into future research directions.