MAE是一种使用自监督预训练策略的ViT，通过遮蔽输入图像中的补丁，然后预测缺失区域进行子监督的与训练。 尽管该方法既简单又有效，但 MAE 预训练目标目前仅限于单一模态——RGB 图像——限制了在通常呈现多模态信息的实际场景中的应用和性能。在新论文 MultiMAE: Multi-modal Multi-task Masked Autoencoders 中，...
MultiMAE(Multi-modal Multi-task Masked Autoencoders)是一种多模态多任务掩码自编码器,旨在通过引入多模态输入和多任务输出,提升自编码器的预训练效果。 核心特点: 多模态输入:与传统的MAE(Masked Autoencoder)相比,MultiMAE不仅接受RGB图像作为输入,还可以接受其他模态的数据,如深度图、语义分割图等。这种多模态输...
MAE是一种使用自监督预训练策略的ViT,通过遮蔽输入图像中的补丁,然后预测缺失区域进行子监督与训练。尽管该方法既简单又有效,但 MAE 预训练目标目前仅限于单一模态——RGB 图像——限制了在通常呈现多模态信息的实际场景中的应用和性能。 ...
We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optionally accept additional modalities of information in the input besides the RGB image (hence "multi-modal"), and II...
在新论文 MultiMAE: Multi-modal Multi-task Masked Autoencoders 中,来自瑞士洛桑联邦理工学院 (EPFL) 的团队提出了 Multi-modal Multi-task Masked Autoencoders (MultiMAE),也是一种预训练策略,可以对掩码进行自动编码处理并执行多模态和多任务的训练。MultiMAE 使用伪标签进行训练,使
Masked autoencoders (MAEs) are a self-supervised pretraining strategy for vision transformers (ViTs) that masks-out patches in an input image and then predicts the missing regions. Although the approach is both simple and effective, the MAE...
MAE是一种使用自监督预训练策略的ViT,通过遮蔽输入图像中的补丁,然后预测缺失区域进行子监督的与训练。尽管该方法既简单又有效,但 MAE 预训练目标目前仅限于单一模态——RGB 图像——限制了在通常呈现多模态信息的实际场景中的应用和性能。 在新论文 MultiMAE: Multi-modal Multi-task Masked Autoencoders 中,来自...
Multi-task learning is a popular machine learning approach that enables simultaneous learning of multiple related tasks, improving algorithmic efficiency and effectiveness. In the hard parameter sharing approach, an encoder shared through multiple tasks
Multi-view-AE: An extensive collection of multi-modal autoencoders implemented in a modular, scikit-learn style framework. autoencoder representation-learning multi-modal variational-autoencoder multiview multiviewae multi-modal-autoencoder mvae multi-modal-variational-autoencoder multivae Updated Feb ...
We propose a pre-training strategy called Multi-modal Multi-task Masked Autoencoders (MultiMAE). It differs from standard Masked Autoencoding in two key aspects: I) it can optionally accept additional modalities of information in the input besides the RGB image (hence "multi-modal"), and II...