摘要 多模态对比表示(Multi-modal Contrastive Representation, MCR)学习的目标:将不同模态编码至一个语义对齐的共享空间。 这一范式在跨越多种模态的下游任务上展现出显著的泛化能力。然而,对于大量高质量数据对的依赖限制了此类方法在更多模态上的进一步发展。 本文提出一种用于学习MCR的高效训练方法C-MCR,该方法无...
In this context, we introduce a novel multi-modal contrastive learning-based pipeline to facilitate learning joint representations for the two retinal imaging modalities. After self-supervised pre-training on 153,306 scan pairs, we show that such a pre-training framework can provide both a ...
模态(modal)是事情经历和发生的方式,我们生活在一个由多种模态(Multimodal)信息构成的世界,包括视觉信息、听觉信息、文本信息、嗅觉信息等等,当研究的问题或者数据集包含多种这样的模态信息时我们称之为多模态问题,研究多模态问题是推动人工智能更好的了解和认知我们周围世界的关键。 什么是多模态 1.1 模态 模态是指一...
AT和PT分别输入到两个MLP-Mixer网络中,最后通过全局平均池化层输出振幅特征(AF)和相位特征(PF),即: Multi-modal features contrastive classification 过拟合就是损失函数极小但泛化性能差的情况。落实在分类问题上就是训练集的损失函数值很小,但是验证集/测试集上的损失函数值很大。 如图1所示,我们的混合网络的输出...
Scaling Up Visual and V-L Representation Learning With Noisy Text Supervision 57:53 Grounded Visual Generation 58:28 MDETR: Modulated Detection for End-to-End Multi-Modal Understanding 01:13:28 A Truly Unbiased Model 01:43:33 Visual Recognition beyond Appearances, and its Robotic Application...
报告嘉宾:朱磊 (山东师范大学)报告时间:2021年09月15日 (星期三)晚上20:00 (北京时间)报告题目:Multi-modal Hash Representation Learning个人主页:https://sites.google.com/site/homepageleizhu报告地址:http://valser.org/article-455-1.html, 视频播放量 1151、弹
2022, Jisuanji Yanjiu yu Fazhan/Computer Research and Development Multi-modal Contrastive Representation Learning for Entity Alignment 2022, Proceedings - International Conference on Computational Linguistics, COLING View all citing articles on Scopus☆...
Figure 1: The pervasive modality gap in multi-modal contrastive representation learningHow do we explain Modality Gap? A three-part explanationn.While it might seem reasonable to attribute the gap to differences in data distributions or to the different encoder architectures, we showed that these ...
报告嘉宾:王淑君 (香港理工大学) 报告时间:2024年8月28日 (星期三)晚上20:30 (北京时间) 报告题目:Multi-modal Representation Learning for Medical Data Analysis 报告人简介: Dr Emma Shujun WANG is an Assistant Professor at PolyU BME. Before that, she was a Research Associate in the Department of ...
2.2CLIP(Contrastive Language–Image Pre-training) CLIP直接利用从互联网爬取的 400 million 个image-text pair 进行图文匹配任务的训练,并将其成功迁移应用于30个现存的计算机视觉分类。做法上我认为CLIP是比较简单和直观的,更多地可以归为大数据的胜利。