Recently, the Vision Transformer (ViT), which applied the transformer structure to the image classification task, has outperformed convolutional neural networks. However, the high performance of the ViT results from pre-training using a large-size dataset such as JFT-300M, and its dependence on a...
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...
初始化和超参数:Transformer对初始化比较敏感,有些方法会不收敛,最终选择截断的正态分布对参数进行初始化。 数据增强:Transformer的训练需要大量的数据,想要在不太大的数据集上取得好性能,就需要大量的数据增强 优化器和正则化等:AdamW不SGD性能更好。作者发现Transformer对优化器的超参数很敏感,试了多组lr...
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...
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?
当增加输入图像的分辨率时,我们保持 patch size,因此 patch 的数量 发生了变化。由于Transformer结构的原因,当 发生变化时,模型的权重不需要做出任何变化也可以以同样的方式计算出Query,Key和Value的值,所以Visual transformer适用于任何长度的sequence。但是位置编码不行,位置编码的长度是 ,当...
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...
[ICLR 2024] CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction [Paper] VLM Knowledge Distillation for Other Vision Tasks [ICLR 2024] FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition [Paper][Project] [ICLR 2024] AnomalyCLIP: Object-ag...
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...
AI,Computer Vision,NLP,Transformer,Trends 10 AI Project Ideas in Computer Vision- Nov 16, 2021. The field of computer vision has seen the development of very powerful applications leveraging machine learning. These projects will introduce you to these techniques and guide you to more advanced pract...