称为“k-disks”，用于标记轨迹数据，使得可以使用小词汇量对Waymo Open Dataset进行标记，以及一个基于T...
Improving Single Modal and Multimodal Biometric Authentication Using F-ratio Client-Dependent Normalisation This study investigates a new client-dependent normalisation to improve a single biometric authentication system, as well as its effects on fusion. There e... N Poh,S Bengio - 《Idiap》 被引...
The fundamental difference between multimodal AI and traditional single-modal AI is the data. A unimodal AI is restricted to processing a single type of data or source, such as text, images or audio and can't understand complex relationships across different data types. For example, a financial...
第一种是Multimodal Transport，是以两种及以上运输方式完成的货物运输，第二种是Intermodal Transport，货物...
We used a single NVIDIA GeForce RTX 2080 Ti (11 GB) GPU for training our model. The latter consists of 29.6M trainable parameters. The time taken for training our model on such a setup was approximately 1.94 hours. In the second stage, the generative model serves as a reconstruction prior...
To further test the usability of MultiVI’s imputation, we next explored a scenario in which the multi- and single-modal data come from different biological conditions. In this case, we resorted to the PBMCs dataset collected under the DOGMA-seq protocol7. In this dataset, PBMCs are profile...
方法:本文提出多模态变分自编码器 (MVAE),利用专家相乘前向网络(product-of-expert inference network)和子采样训练(sub-sampled training paradigm)解决multi-modal inference problem. 本文通过共享参数来学习各种模态缺失的情况。此外,MVAE可直接用于弱监督及不完全监督中。
In this paper, we present a novel histogram based method for estimating and maximising mutual information (MI) between two multi-modal and possibly multi-b... R Fransens,C Strecha,LV Gool - 《Colloquium Mathematicum》 被引量: 36发表: 2004年 加载更多研究...
Both methods learn a single latent space to represent the multi-modal data. In contrast, DMVAE26 learns a disjoint private and shared space in a multi-modal setting. The self attention operation of Transformers27 provides a natural mechanism to connect multimodal signals. Many works have studied...
Single-cell datasets continue to grow in size, posing computational challenges for dealing with expanded scale, extended modality and inevitable batch effects. Deep learning-based approaches have recently emerged to address these points by deriving nonli