只要你不是与世隔绝的深度炼丹者,应该都知道前阵子恺明大神的佳作MAE(Masked Autoencoders Are Scalable Vision Learners),自双11那天挂到 arXiv 后,江湖上就开始大肆吹捧:'yyds'、'best paper 预定' 什么的满天飞.. 造成这一现象最主要原因还是大神本身的光环所致,另外就是大家看到 paper 中展示的 mask 掉图像...
(2) MAE Encoder MAE中的编码器是一种ViT,但仅作用于可见的未被Mask的块。类似于标准ViT,该编码器通过线性投影于位置嵌入对块进行编码,然后通过一系列Transformer模块进行处理。然而,由于该编解码仅在较小子集块(比如25%)进行处理,且未用到掩码Token信息。这就使得我们可以训练一个非常大的编码器。 (3) MAE De...
Visual object tracking vision transformer masked autoencoder visual representation learning 1. Introduction Single object tracking is a fundamental task within the field of computer vision, aiming to persistently track an arbitrary target object across a video sequence starting from its initial condition [...
在计算机视觉领域，MAE（Masked Autoencoders）作为自监督学习的新兴力量，凭借其独特的优势和创新设计，正在重塑我们对预训练的理解。MAE的核心在于其非对称的ViT（Vision Transformer）架构，它通过仅编码可见的patch，而让解码器处理编码器输出和mask tokens，展现出强大的扩展性和灵活性。卓越表现与迁移能力...
ViT lacks the inductive bias inherent to convolution making it require a large amount of data for training. This results in ViT not performing as well as CNNs on small datasets like medicine and science. We experimentally found that masked autoencoders (MAE) can make the transformer focus more...
摘要: vision transformer (ViT) based multimodal learning methods have been proposed to improve the robustness of face anti-spoofing (FAS) systems. However, there are still no works to e...关键词: Multimodal Face anti-spoofing Adaptive multimodal adapter Masked autoencoder ...
In this paper, we show that masked autoencoders are also scalable self-supervised learners for image processing tasks. We first present an efficient Transformer model considering both channel attention and shifted-window-based self-attention termed CSformer. Then we develop an effective MAE ...
For better transferring the learned multi-scale representations to downstream tasks, we utilize the popular Swin Transformer with larger-window size as the encoder of the proposed Mix- MAE [27, 28]. Figure 1 illustrates the proposed framework. Mixed Training Inp...
autoencoder: it has an encoder that maps an input to a latent representation and a decoder that reconstructs the input. denoising autoencoders(DAE)是一类自动编码器,它破坏输入信号,并学习重建原始的、未破坏的信号。 (论文中这部分非常简洁)
MAE 的全称是 Masked Autoencoder, 和 BERT 模型差别还是挺大的。特别说明一下, 这部分所说的 encoder 和 decoder 都是AutoEncoder 中的概念, 和 Transformer 没有关系。 和AutoEncoder 类似, 预训练的网络架构分成 encoder 和 decoder 两部分, 用的都是 ViT 模型。具体的做法如下: 对于输入的图片, 随机选择 ...