Vision Transformer for Small-Size Datasets 来自 arXiv.org 喜欢 0 阅读量: 370 作者:SH Lee,S Lee,BC Song 摘要: Recently, the Vision Transformer (ViT), which applied the transformer structure to the image classification task, has outperformed convolutional neural networks. However, the high ...
Vision Transformer for Small Datasets Dino Accessing Attention Research Ideas Efficient Attention Combining with other Transformer improvements FAQ Resources Citations Vision Transformer - Pytorch Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single ...
Conv + Transformer(卷积+Transformer) 1、SATA: "Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets", WACV, 2023 2、SparK: "Sparse and Hierarchical Masked Modeling for Convolutional Representation Learning", ICLR, 2023 3、MOAT: "MOAT: Alternating Mobile Convolution and Attenti...
具体方法1:使Deep Vision Transformer易于收敛,并能提高精度的LayerScale LayerScale这个操作的目的也很直接,就是为了使得vision transformer在训练时更稳定。作者首先对比了如下图20所示的4种不同的transformer blocks的正则化策略,看看它们哪个有助于提升优化的效果。 图20:4种不同的transformer blocks的正则化策略 图2...
Visual Transformer for Task-aware Active Learning [paper] [code] Efficient Training of Visual Transformers with Small-Size Datasets [paper] Reveal of Vision Transformers Robustness against Adversarial Attacks [paper] Person Re-Identification with a Locally Aware Transformer [paper] [Refiner] Refiner: Re...
Transformer block for images:Multi-head Self Attention layers 之后往往会跟上一个 Feed-Forward Network (FFN) ,它一般是由2个linear layer构成,第1个linear layer把维度从 维变换到 维,第2个linear layer把维度从 维再变换到 此时的Transformer block是不考虑位置信息的,即一幅图片只要内容不变,patches的顺序...
来源:https://github.com/google-research/vision_transformer 尽管ViT full-Transformer架构是视觉处理任务的一个很有前景的选择,但当在中等大小的数据集(如ImageNet)上从头开始训练时,ViT的性能仍然不如类似大小的CNN替代方案(如ResNet)。 2021,在ImageNet上从头开始训练时,ViT与ResNet和MobileNet的性能基准比较,来源...
但是反观基于Transformer的模型ViT,DeiT,它们不论是base,small还是tiny尺寸的模型,大小都是只有12个layers。如果直接把depth加深的话,模型的性能会迅速地饱和,甚至32层ViT的性能还不如24层的模型,如下图1所示。那么一个自然而然的问题就是:能不能像CNN一样,采取一些什么手段来加深Transformer模型呢?
3.3 Transformer vs. CNNs CNNs provide promising results for image analysis, while Vision Transformer has shown comparable even superior performance when pre-training or scaled datasets are available (Dosovitskiy et al., 2020). This raises a question on the differences about how Transformers and CN...
Visual Transformer Pruning 量化 和CNN比,难点主要在量化self-attention上。 PTQ4ViT: Post-Training Quantization Framework for Vision Transformers FQ-ViT: Fully Quantized Vision Transformer without Retraining Q-ViT: Fully Differentiable Quantization for Vision Transformer TerViT: An Efficient Ternary Vision Tra...