Kotecha, “Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions,” Information Fusion, 2022. [13] T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction. Spri...
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Transformer的系统综述,多模态Tran...
BERTERS: Multimodal representation learning for expert recommendation system with transformers and graph embeddingsMultimodal representation learningExpert recommendation systemTransformerGraph embeddingAn expert recommendation system suggests relevant experts of a particular topic based on three different scores ...
Lecture5.1-MultimodalTransformers-Part1(CMUMultimodalMachineLearning,Fall2023) 2 -- 1:08:28 App Lecture7.2-MultimodalInferenceandKnowledge(CMUMultimodalMachineLearning,Fall2023 5 -- 1:11:57 App Lecture4.1-MultimodalAlignment(CMUMultimodalMachineLearning,Fall2023) 4 -- 1:13:21 App Lecture4.2-AlignedRepr...
Multiple-Instance-Learning-NCE (MIL-NCE): 对于video-text pairs,利用 MIL-NCE 进行学习,即:比对一个视频的输入和多个时序上与视频输入邻近的文本输入; 因此,总体的对比学习损失函数为两个损失函数的平衡: 4. Pre-train dataset: internet videos: 1.2M unique videos, each providing multiple clips with audio...
Multiple-Instance-Learning-NCE (MIL-NCE): 对于video-text pairs,利用 MIL-NCE 进行学习,即:比对一个视频的输入和多个时序上与视频输入邻近的文本输入; 因此,总体的对比学习损失函数为两个损失函数的平衡: 4. Pre-train dataset: internet videos: 1.2M unique videos, each providing multiple clips with audio...
Multi-level correlation mining framework with self-supervised label generation for multimodal sentiment analysis Unimodal feature fusionLinguistic-guided transformerSelf-supervised label generationFusion and co-learning are major challenges in multimodal sentiment analysis. ... Z Li,Q Guo,Y Pan,... - 《...
今天介绍一篇论文《TRAJEGLISH: LEARNING THE LANGUAGE OF DRIVING SCENARIOS》，来自NVIDIA，多伦多大学，...
from: https://www.youtube.com/watch?v=helW1httyO8&list=PLki3HkfgNEsKPcpj5Vv2P98SRAT9wxIDa搭配视频: https://www.bilibili.com/video/BV1cN411S7VY/?vd_source=21cce77bb69d40a81e0d37999f2da0c2Multimodal Learning at CVPR 2022===
This repository is built to explore the potential and extensibility of transformers for multimodal learning. We utilize the advantages of Transformers to deal with length-variant sequences. Then we propose theData-to-Sequencetokenization following a meta-scheme, then we apply it to 12 modalities incl...