In 2024, significant progress has been made in the field of “multimodal + reinforcement learning”. Researchers have proposed various innovative methods to integrate data from different modalities to enhance the performance and applicability of reinforcement learning algorithms. For example, methods mentioned in the literature include utilizing Masked Multimodal Learning to achieve the fusion of visual … Read more
Click on the above“Beginner’s Guide to Vision” to choose to add to favorites or pin. Essential content delivered promptly The Transformer network architecture, as an exceptional neural network learner, has achieved great success in various machine learning problems. With the booming development of multimodal applications and multimodal big data in recent years, multimodal learning based … Read more
Source: ZHUANZHI This article is about 1000 words and is recommended to read in 5 minutes. Research on Large Multimodal Models (LMMs) has become a focal point in the field of deep learning, demonstrating its importance in contemporary research. LMMs can process data from different modalities, enhancing predictive capabilities by leveraging complementary information to perform … Read more
Source: Multimodal Machine Learning and Large Models This article is approximately 1800 words long and is recommended for a 10-minute read. This article introduces the evaluation of DeCUR in three common multimodal scenarios (Radar Optical, RGB Elevation, and RGB Depth) and demonstrates its continuous improvement, regardless of architecture, as well as in multimodal and modality-missing … Read more
Abstract: Multimodal learning analytics provides a new perspective for measuring and evaluating learning in complex environments. This article analyzes the research community, themes, fields, evolution, and hotspots of multimodal learning analytics using various analytical methods, based on a database that covers various academic organizations and papers on multimodal learning analytics. The research results show that … Read more
Follow the public account “ML_NLP“ Set as “Starred“, delivering heavy content promptly! Reprinted from | Zhihu Author | Liang Chao Wei from Summer ResortOriginal link | https://zhuanlan.zhihu.com/p/412126626 01 – Background In the traditional NLP unimodal field, the development of representation learning is relatively mature. However, in the multimodal field, due to the scarcity of high-quality … Read more
Delivering NLP technical insights to you every day! Reprinted from | PaperWeekly Author | Albert Yang Affiliation | Amazon/Georgia Tech Research Direction | NLP As a procrastinator, I’m writing this summary of the ACL conference just before NAACL 🙁 . At the onsite conference, I saw my former boss Bonnie Webber (ACL 2020 Lifetime Achievement … Read more
This article is about 14,500 words, and it is recommended to read for more than 15 minutes. This article will share the research achievements of Ant Group's multimodal cognitive team in the field of video multimodal retrieval over the past year. [ Introduction ] This article will share the research achievements of Ant Group’s multimodal … Read more
Introduction Graph Learning is a machine learning method that studies and applies graph-structured data. In graph learning, data is represented as a graph consisting of nodes and edges, where nodes represent entities or objects, and edges represent the relationships or connections between them. Therefore, graph learning is particularly suitable for multi-scale analysis, modeling, and simulation … Read more
Education and Teaching Application Of Multimodal Artificial Intelligence In Nursing Education Peng Wenli, Cheng Xinhua, Zhang Xian (Chongqing University of Humanities, Science and Technology, School of Nursing) Abstract: With the continuous advancement of technology and the rapid development of artificial intelligence, the application of multimodal artificial intelligence in nursing education has become a trend. The … Read more