Swin Transformer
E435882
Swin Transformer is a hierarchical vision transformer architecture that uses shifted windows for efficient and scalable image recognition and related computer vision tasks.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Swin Transformer canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4389210 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Swin Transformer Context triple: [Hugging Face Transformers, supportsModelType, Swin Transformer]
-
A.
ResNeXt
ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition tasks.
-
B.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
C.
PWSFTviT
PWSFTviT is the renowned Łódź Film School in Poland, one of Europe’s leading film and television academies known for training many acclaimed filmmakers.
-
D.
ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
-
E.
RetinaNet
RetinaNet is a deep learning–based one-stage object detection model known for its focal loss function, which effectively addresses class imbalance to achieve high accuracy and speed.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Swin Transformer Target entity description: Swin Transformer is a hierarchical vision transformer architecture that uses shifted windows for efficient and scalable image recognition and related computer vision tasks.
-
A.
ResNeXt
ResNeXt is a deep convolutional neural network architecture that extends ResNet by using grouped convolutions and a split-transform-merge strategy to improve accuracy and efficiency in image recognition tasks.
-
B.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
-
C.
PWSFTviT
PWSFTviT is the renowned Łódź Film School in Poland, one of Europe’s leading film and television academies known for training many acclaimed filmmakers.
-
D.
ShuffleNetV2
ShuffleNetV2 is a lightweight convolutional neural network architecture designed for efficient image classification on resource-constrained devices, emphasizing speed and low computational cost.
-
E.
RetinaNet
RetinaNet is a deep learning–based one-stage object detection model known for its focal loss function, which effectively addresses class imbalance to achieve high accuracy and speed.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
hierarchical transformer model
ⓘ
vision transformer architecture ⓘ |
| architectureType |
hierarchical
ⓘ
window-based transformer ⓘ |
| benchmarkPerformance |
state-of-the-art on ADE20K semantic segmentation at introduction
ⓘ
state-of-the-art on COCO object detection at introduction ⓘ |
| coAuthor |
Baining Guo
NERFINISHED
ⓘ
Han Hu NERFINISHED ⓘ Stephen Lin NERFINISHED ⓘ Yixuan Wei NERFINISHED ⓘ Yue Cao NERFINISHED ⓘ Yutong Lin NERFINISHED ⓘ Zheng Zhang NERFINISHED ⓘ |
| designedFor |
dense prediction tasks
ⓘ
image recognition ⓘ instance segmentation ⓘ object detection ⓘ semantic segmentation ⓘ |
| field |
computer vision
ⓘ
deep learning ⓘ machine learning ⓘ |
| firstAuthor | Ze Liu NERFINISHED ⓘ |
| hasComponent |
MLP feed-forward network
ⓘ
Swin Transformer block ⓘ patch merging layer ⓘ patch partition layer ⓘ window-based multi-head self-attention ⓘ |
| hasVariant |
Swin-B
NERFINISHED
ⓘ
Swin-L NERFINISHED ⓘ Swin-S NERFINISHED ⓘ Swin-T NERFINISHED ⓘ |
| influenced |
Swin Transformer V2
NERFINISHED
ⓘ
window-based vision transformer architectures ⓘ |
| inputType | image patches ⓘ |
| inspiredBy | Vision Transformer (ViT) NERFINISHED ⓘ |
| introducedBy | Microsoft Research Asia NERFINISHED ⓘ |
| introducedIn | 2021 ⓘ |
| introducedInPaper | Swin Transformer: Hierarchical Vision Transformer using Shifted Windows NERFINISHED ⓘ |
| keyFeature |
cross-window connection via window shifting
ⓘ
hierarchical representation ⓘ linear computational complexity with image size ⓘ local self-attention within windows ⓘ shifted window attention ⓘ |
| outputType | multi-scale feature maps ⓘ |
| publishedAt | ICCV 2021 NERFINISHED ⓘ |
| usedAs |
backbone for instance segmentation
ⓘ
backbone for object detection ⓘ backbone for semantic segmentation ⓘ |
| usesMechanism |
layer normalization
ⓘ
multi-head self-attention ⓘ residual connections ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Swin Transformer Description of subject: Swin Transformer is a hierarchical vision transformer architecture that uses shifted windows for efficient and scalable image recognition and related computer vision tasks.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.