基于以上分析,对于视觉representation的学习,我们提出了一种简单,高效,可扩展形式的 masked autoencoder(MAE)。 我们的 MAE 随机遮住输入图像的一些块,并且在像素空间上重建这些损失的块。这里包含一个非对称的encoder-decoder设计。我们的 encoder 值处理 patchs 的可见部分,而 decoder 是轻量级的,并且从隐含的 represe...
该论文属于MAE（Masked Autoencoders）方法向自动驾驶轨迹预测领域的拓展应用。一、论文速览 本文第一次将...
transformer的Encoder模块输入未mask的输入特征以及位置编码,输出编码特征 ,Decoder模块添加可学习的mask token向量,旨在使用decoder模块学习到被mask的特征,输出的 中含有全部特征(包括mask和未mask的特征) Reconstruction 由于输入数据是与当前帧3的偏差,预测轨迹仍然是预测与当前帧的偏差。 Loss function: 历史轨迹和未来...
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 [1–3]. In particular, the user provid...
; CE: cross-entropyloss,MSE:meansquareerrorloss. *: We repeat and balance each class to 50% of the size of the unknown class. †: For ViT-S, We use a learning rate of 0.0005 on AS-2M FT and 0.002 on AS-20K FT as we find larger learning rates work better for ViT-S encoder....
The additional classification loss after the encoder speeds up convergence and improves reconstruction efficiency during training. The mean squared error between the input image x and reconstructed image xˆ is used as the reconstruction loss function Lrtec(x, xˆ). 3.2. ...
Each reconstruction is compared to the ground truth image through a mean square error (MSE) loss function to guided self-supervised learning17. Network architecture PI-MAE is an asymmetric autoencoder designed for the reconstruction of original signals from partially observed inputs, incorporating the...
Finally, the teacher net performs knowledge distillation, and the student net receives the loss function transmitted from the teacher net for optimization. The experimental results show that the proposed method outperforms other methods on the FBP task, improves FBP accuracy, and can be widely ...
masked autoencoder(MAE) are scalable self-supervised learners for computer vision. MAE方法很简单:随机地mask图片中的一些patch,然后再去reconstruct这些丢失的像素。有两个核心的设计,1)有一个非对称的encoder-decoder架构,encoder只作用在可见的这些patch里面(也就是被mask的patch,encoder是不会对它进行编码的),...
MAE Decoder此处的输入是token进入encoder后提取到的特征 + mask token。 此处,mask token的信息主要来自position embedding, 否则信息将是空。 Reconstruction targetloss function 定义是 原始图像 与 重建图像的 MSE; 此外,这块只针对masked patches Implementation此处不确定顺序这个概念是否很重要。因为,论文中token和ma...