Statements (65)
| Predicate | Object |
|---|---|
| gptkbp:instance_of |
gptkb:microprocessor
|
| gptkbp:bfsLayer |
5
|
| gptkbp:bfsParent |
gptkb:Swin_Transformer
|
| gptkbp:applies_to |
image classification
object detection semantic segmentation |
| gptkbp:based_on |
gptkb:Transformers_character
|
| gptkbp:competes_with |
gptkb:Vision_Transformers
|
| gptkbp:developed_by |
gptkb:Microsoft_Research
|
| gptkbp:has |
attention mechanism
layer normalization hierarchical feature maps multi-scale feature representation |
| gptkbp:has_achievements |
state-of-the-art performance
|
| https://www.w3.org/2000/01/rdf-schema#label |
Swin-T
|
| gptkbp:improves |
computational efficiency
|
| gptkbp:introduced |
gptkb:2021
|
| gptkbp:is_adopted_by |
research institutions
startups tech companies |
| gptkbp:is_compatible_with |
NVIDIAGP Us
TP Us |
| gptkbp:is_documented_in |
research papers
technical reports |
| gptkbp:is_enhanced_by |
data augmentation techniques
regularization methods |
| gptkbp:is_evaluated_by |
gptkb:Open_Images_dataset
gptkb:COCO_dataset gptkb:Cityscapes_dataset gptkb:LSUN_dataset Kaggle competitions AD E20 K dataset PASCALVOC dataset Image Net-21 K dataset |
| gptkbp:is_implemented_in |
gptkb:Graphics_Processing_Unit
gptkb:Py_Torch |
| gptkbp:is_influenced_by |
gptkb:television_channel
gptkb:Vision_Transformers |
| gptkbp:is_known_for |
high accuracy
modular design scalability robustness to noise layer-wise training flexibility in various tasks |
| gptkbp:is_optimized_for |
GPU acceleration
|
| gptkbp:is_part_of |
deep learning frameworks
AI research community computer vision benchmarks AI model zoo image processing pipeline Swin Transformer family |
| gptkbp:is_popular_in |
gptkb:academic_research
industry applications |
| gptkbp:is_supported_by |
tutorials
community contributions open-source libraries |
| gptkbp:is_used_by |
gptkb:physicist
gptkb:software |
| gptkbp:is_used_for |
computer vision tasks
|
| gptkbp:is_used_in |
semi-supervised learning
self-supervised learning |
| gptkbp:supports |
transfer learning
|
| gptkbp:training |
gptkb:Image_Net_dataset
large-scale datasets |
| gptkbp:utilizes |
shifted windows
|