paper: 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...
视觉策略——虽然典型的视觉注意在每一步都指向一个单一的图像区域,但Zha等人[66]提出的方法引入了一个子策略网络,通过LSTM编码历史视觉动作(例如,以前关注的区域),从而也顺序解释视觉部分,作为下一个视觉动作的上下文。 几何变换——Pedersoli等人[67]提出使用空间transformers,通过以弱监督的方式回归区域建议,来生成...
文献著作信息: Training data-efficient image transformers & distillation through attention https://github.com/facebookresearch/deit/blob/main/README_deit.md https://arxiv.org/pdf/2012.12877.pdf 2021 我的收获: 最好使用较低的训练分辨率,并以更大的分辨率微调网络Fixing the train-test resolution discrepa...
提出一种掩码图像建模任务,以自监督方式预训练视觉transformer。对BEIT进行了预训练,并对下游任务进行微...
1. 论文和代码地址 BEIT: BERT Pre-Training of Image Transformers 论文地址:https://arxiv.org/abs...
[2] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding [3] BEIT: BERT Pre-Training of Image Transformers [4] Generative Pretraining from Pixels [5] Extracting and composing robust features with denoising autoencoders ...
64-An Empirical Study of Training Self-Supervised Vision Transformers MoCov1通过dictionary as a queue和momentum encoder和shuffle BN三个巧妙设计,使得能够不断增加K的数量,将Self-Supervised的威力发挥的淋漓尽致。MoCov2在MoCov1的基础上,增加了SimCLR实验成功的tricks,然后反超SimCLR重新成为当时的SOTA,FAIR和Goo...
transformers for image recognition at scale代码讲解Transformers for Image Recognition at Scale Introduction In recent years, deep learning models have achieved remarkable performance in image recognition tasks. However, as the scale and complexity of image datasets continue to increase, traditional ...
[2] Touvron, Hugo, et al. “Training data-efficient image transformers & distillation through attention.” International Conference on Machine Learning. PMLR, 2021. [3] Gulati A, Qin J, Chiu C C, et al. Conformer: Convolution-augmented transformer for speech recognition[J]. arXiv preprint ar...
Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images Qiang Wang Ahsan R. Akram Marta Vallejo npj Imaging (2024) Current status and prospects of artificial intelligence in breast cancer pathology: convolutional neural networks to prospective Vision Transformers Ay...