Contrastive representation learningMRIRadiology reportExplainabilityGliomaDespite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. That is mainly due to the fact ...
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Expertsarxiv.org/abs/2206.02770 论文源码未释放。该论文是谷歌提出的首个多模态稀疏化模型LIMoE,这是谷歌对于稀疏化模型的又一篇工作,在零样本学习和降低计算成本上有不错的成果。 首先,介绍一下什么是稀疏化:稀疏性(Sparsity),指的...
arXiv:2308.15469v1 [cs.CV] 29 Aug 2023Multimodal Contrastive Learning and Tabular Attention for AutomatedAlzheimer’s Disease PredictionWeichen HuangSt Andrew’s CollegeDublin, Irelandw.huang@students.st-andrews.ieAbstractAlongside neuroimaging such as MRI scans and PET,Alzheimer’s disease (AD) datas...
The “deep learning” era (2010s until …),促使多模态研究发展的关键促成因素有4个,1)新的大规模多模态数据集,2)GPU快速计算,3)强大的视觉特征抽取能力,4)强大的语言特征抽取能力。 表示学习三篇参考文献 Multimodal Deep Learning [ICML 2011] Multimodal Learning with Deep Boltzmann Machines [NIPS 2012] ...
Deep learning-based approaches have recently emerged to address these points by deriving nonlinear cell embeddings. Here we present contrastive learning of cell representations, Concerto, which leverages a self-supervised distillation framework to model multimodal single-cell atlases. Simply by discriminating...
A Simple Framework for Contrastive Learning of Visual Representations 1. 论文摘要 提出了一个针对图像表征的基于contrastive learning 的简单框架。主要结论:(1) 数据增强的组成对定义有效的预测任务十分重要。(2) 通过在表征与contrastive loss 之间引入一个非线性变换对于学习的表征质量有很大...A...
Paper tables with annotated results for Unlearning Backdoor Threats: Enhancing Backdoor Defense in Multimodal Contrastive Learning via Local Token Unlearning
Abstract: Multimodal contrastive learning (MCL) has shown remarkable advances in zero-shot classification by learning from millions of image-caption pairs crawled from the Internet. However, this reliance poses privacy risks, as hackers may unauthorizedly exploit image-text data for model training, pot...
learning-based patient-specific CT organ dose estimation method namely, multimodal contrastive learning with Scout images (Scout-MCL). Our proposed Scout-MCL gives accurate and realistic dose estimates in real-time and prospectively, by learning from multi-modal information leveraging image (lateral and...
Directly learning representation from long-form videos and language may benefit many long-form video-language understanding tasks. However, it is challenging due to the difficulty of modeling long-range relationships and the heavy computational burden caused by more frames. In ...