论文类型:Survey Paper 论文链接:Multimodal Learning With Transformers: A Survey | IEEE Journals & Magazine | IEEE Xplore 整体评价:这是一篇关于使用Transformer进行多模态学习的综述文章。文章主要内容包括多模态学习的背景、Transformer生态系统和多模态大数据时代,Vanilla Transformer,Vision Transformer和多模态Transforme...
Self-Supervised Multimodal Learning: A Survey[J]. arXiv preprint arXiv:2304.01008, 2023. 2.内容 一.解决的问题:多模态数据配对的问题,例如通过文字匹配图像。 二.分类:目标函数、数据对齐和模型架构。 第一类:目标函数 实例判别 在单模态学习中,实例判别 (ID) 将原始数据中的每个实例视为一个单独的类,并...
Multiple Kernel learning(MKL),多核学习(将不同的核用于不同的数据模态/视图) Graphical models,图模型后续可以看看 Neural Networks,神经网络 循环神经网路,进行端到端的训练 八、共同学习 Co-learning 解释:通过利用来自另一种(资源丰富)模态的知识来帮助(资源贫乏)模态建模;辅助模态(helper modality)通常只参与模...
“multimodal fusion is the concept of integrating information from multiple modalities with the goal of predicting an outcome measure: a class (e.g., happy vs. sad) through classification, or a continuous value (e.g., positivity of sentiment) through regression.”融合还有更宽泛的定义,而综述...
However, to the best of our knowledge, only a handful of studies have been conducted to improve system performance utilizing multimodal data. In this survey paper, we identify the significance of this emerging research topic of multimodal federated learning (MFL) and present a literature review on...
Multimodal Machine Learning:A Survey and Taxonomy 多模态机器学习:综述与分类,程序员大本营,技术文章内容聚合第一站。
Deep multimodal representation learning: a survey Multimodal representation learning, which aims to narrow the heterogeneity gap among different modalities, plays an indispensable role in the utilization o... W Guo,J Wang,S Wanga - 《IEEE Access》 被引量: 0发表: 2019年 A survey of multimodal hy...
The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. The growing potential
综述一:A Survey on Multimodal Large Language Models 论文链接:https://arxiv.org/pdf/2306.13549.pdf 项目链接:https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models 2024年4月1号更新的一篇paper。 一、多模态LLM的组成部分 常见的多模态LLM结构: ...
People are quick to anthropomorphize, attributing human characteristics to non-human agents1. The tendency to anthropomorphize has only intensified with the advent of large language models (LLMs)2. LLMs apply deep learning techniques to generate text3, learning from vast datasets to produce respon...