Methods › Computer Vision › Vision Transformers
Vision Transformers
The archive attaches this collection's text per method and the copies differ: 6 distinct texts across 42 of the 42 methods here. All are shown, most-carried first (a tie goes to the text carrying Papers with Code's collection boilerplate, then to the longer text); no vote is taken between them.
Text 1, carried by 37 of 42 methods:
Vision Transformers are Transformer-like models applied to visual tasks. They stem from the work of ViT which directly applied a Transformer architecture on non-overlapping medium-sized image patches for image classification. Below you can find a continually updating list of vision transformers.
According to [1], ViT type models can be further categorized into uniform scale ViTs, multi-scale ViT, hybrid ViTs with convolutions, and self-supervised ViTs. The methods listed below provide a comprehensive overview of ViT models applied to a range of vision tasks.
Text 2, carried by 1 of 42 methods:
Self-Supervised Learning refers to a category of methods where we learn representations in a self-supervised way (i.e without labels). These methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. Below you can find a continuously updating list of self-supervised methods.
Text 3, carried by 1 of 42 methods:
Attention Modules refer to modules that incorporate attention mechanisms. For example, multi-head attention is a module that incorporates multiple attention heads. Below you can find a continuously updating list of attention modules.
Text 4, carried by 1 of 42 methods:
Object Detection Models are architectures used to perform the task of object detection. Below you can find a continuously updating list of object detection models.
Text 5, carried by 1 of 42 methods:
Transformers are a type of neural network architecture that have several properties that make them effective for modeling data with long-range dependencies. They generally feature a combination of multi-headed attention mechanisms, residual connections, layer normalization, feedforward connections, and positional embeddings.
Text 6, carried by 1 of 42 methods:
Image Models are methods that build representations of images for downstream tasks such as classification and object detection. The most popular subcategory are convolutional neural networks. Below you can find a continuously updated list of image models.
Also reached at /methods/category/vision-transformer (Papers with Code's slug for this collection; the archive carries no slugs, so this site's is derived from the name).
Methods
All 42 methods in this collection, most-tagged first. Year is the archive's introduced_year; the archive stores 2000 when it has none, shown here as “–”. Papers counts distinct papers the archive tags with the method. Click a heading to sort.
| Vision Transformer | – | 2,144 |
| Swin Transformer | – | 416 |
| DINO self-DIstillation with NO labels | – | 208 |
| NesT | – | 37 |
| Deformable DETR | – | 35 |
| CCT Compact Convolutional Transformers | – | 33 |
| PVT Pyramid Vision Transformer | – | 28 |
| DPT Dense Prediction Transformer | – | 25 |
| MobileViT | – | 22 |
| CvT Convolutional Vision Transformer | – | 12 |
| TNT Transformer in Transformer | – | 12 |
| T2T-ViT Tokens-To-Token Vision Transformer | – | 11 |
| MoCo v3 | – | 10 |
| LV-ViT | – | 9 |
| MViT Multiscale Vision Transformer | – | 9 |
| CaiT Class-Attention in Image Transformers | – | 5 |
| CoaT Co-Scale Conv-attentional Image Transformer | – | 5 |
| MobileViTv2 | – | 5 |
| Focal Transformers | – | 4 |
| MUSIQ | – | 4 |
| PVTv2 Pyramid Vision Transformer v2 | – | 4 |
| XCiT | – | 4 |
| ConViT | – | 3 |
| CrossTransformers | – | 3 |
| CrossViT | – | 3 |
| VATT | – | 3 |
| BatchFormer Batch Transformer | – | 2 |
| CPVT Conditional Position Encoding Vision Transformer | – | 2 |
| CeiT Convolution-enhanced image Transformer | – | 2 |
| DeepViT | – | 2 |
| LeVIT | – | 2 |
| Colorization Transformer | – | 1 |
| EsViT | – | 1 |
| LocalViT | – | 1 |
| MHMA Multi-Heads of Mixed Attention | – | 1 |
| MobileViTv3 | – | 1 |
| OODformer | – | 1 |
| RegionViT | – | 1 |
| Shuffle-T Shuffle Transformer | – | 1 |
| Twins-PCPVT | – | 1 |
| Twins-SVT | – | 1 |
| Visformer | – | 1 |