MAE:Masked Autoencoders Are Scalable Vision Learners SimMIM:SimMIM: a Simple Framework for Masked Image Modeling OmniMAE:OmniMAE: Single Model Masked Pretraining on Images and Videos PixMIM:PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling ● MAE:MIM当前王者。模型结构见图3。 ● Si...
算法全称为BidirectionalEncoder representation fromImageTransformers (BEiT),提出了 Masked Image Modeling 自监督训练任务的概念,以此来对 ViT 进行训练。如算法概览图(下图)所示,BEiT 预训练中,每一张图片有两种视角:一是图像块 (image patches),如每一小块图像为 16x16 像素;二是离散的视觉标记 (discrete visual...
今天介绍我们在自监督掩码学习(Masked Image Modeling)领域的一篇原创工作 HPM (HardPatchesMining for Masked Image Modeling)。 各种自监督掩码学习方法的性能强烈依赖于人工定义的掩码策略,而我们提出一种新的困难样本挖掘策略,让模型自主地掩码困难样本,提升代理任务的难度,从而获得强大的表征提取能力。目前 HPM 已被 ...
self.mask_patch_size = mask_patch_size self.model_patch_size = model_patch_size # 即4中的kernel = stride = 4 self.mask_ratio = mask_ratio assert self.input_size % self.mask_patch_size == 0 assert self.mask_patch_size % self.model_patch_size == 0 self.rand_size = self.input_s...
基于Masked Image Modeling(MIM)的视觉预训练范式近来吸引了大量关注,具体来讲,MIM 首先随机 mask 输入图像的一部分,然后利用神经网络来预测被 mask 的部分。如何对被 mask 的部分进行表示一直以来是研究人员关注的热点,并没有一个定论,比如在 BEiT[2]中利用 DALL-E[3]的中间表示作为预测目标,在 MAE[4]中直接...
Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, that is, how to design proper pretext tasks for vision dictionary look-up. MIM recently dominates this line of research ...
1CtxMIM: Context-Enhanced Masked ImageModeling for Remote Sensing Image UnderstandingMingming Zhang, Qingjie Liu, Member, IEEE, and Yunhong Wang, Fellow, IEEEAbstract—Learning representations through self-supervisionon unlabeled data has proven highly effective for understandingdiverse images. However, re...
An important goal of self-supervised learning is to enable model pre-training to benefit from almost unlimited data. However, one method that has recently become popular, namely masked image modeling (MIM), is suspected to be unable to benefit from larger data. In this work, we break th...
Han Hu, Yue Cao June 2023 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Related File Masked image modeling (MIM) as pre-training is shown to be effective for numerous vision downstream tasks, but how and where MIM works remain unclear. In this ...
掩码图像建模(Masked Image Modeling,MIM)是自监督视觉表示学习领域的一类算法,通过对输入进行局部掩码,基于未被掩码的部分预测信号(如,归一化像素、离散标记、HOG特征、深层特征、频率特征),MIM可完成语义表示的学习,其受益于自然语言处理中的掩蔽语言建模(Masked Language Modeling,MLM)和ViT的发展。