# Hierarchical Vision Transformer

A Hiera image encoder employing an absolute window position embedding strategy, pre-trained via Masked Image Modeling (MIM), serving as a general-purpose feature extractor or backbone network for downstream tasks.

Hiera is an efficient hierarchical vision Transformer model that excels in image and video tasks with fast runtime

Hiera is a hierarchical vision Transformer model that is fast, powerful, and concise, surpassing existing technologies in image and video tasks while being faster.

Hiera is a hierarchical vision Transformer model that is fast, powerful, and concise, surpassing state-of-the-art performance in a wide range of image and video tasks while significantly improving runtime speed.

Hiera is a hierarchical vision Transformer model that is fast, powerful, and concise, excelling in image and video tasks.

Hiera is a hierarchical vision Transformer model that is fast, powerful, and concise. It surpasses state-of-the-art performance in a wide range of image and video tasks while significantly improving runtime speed.

Hiera is a hierarchical vision Transformer model that combines speed, powerful functionality, and minimalist design, significantly surpassing existing technical standards in image and video tasks with outstanding computational efficiency.

Hiera is a hierarchical vision Transformer model that is fast, powerful, and extremely concise. It surpasses current state-of-the-art techniques in a wide range of image and video tasks while achieving significant speed improvements.

UperNet is a framework for semantic segmentation, combining the Swin Transformer backbone to achieve pixel-level scene understanding

NAT-Small is a hierarchical vision transformer based on neighborhood attention, designed for image classification tasks.

DiNAT-Mini is a hierarchical vision Transformer model based on neighborhood attention mechanism, specifically designed for image classification tasks.

Swin Transformer v2 is a vision Transformer model pre-trained on ImageNet-21k and fine-tuned on ImageNet-1k at 384x384 resolution, featuring hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification and dense recognition tasks through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision transformer model that achieves efficient image classification and dense recognition tasks through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification through hierarchical feature maps and local window-based self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image processing through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification and dense recognition tasks through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification and dense recognition tasks through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image processing through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model that achieves efficient image classification through hierarchical feature maps and local window self-attention mechanisms.

Swin Transformer v2 is a vision Transformer model pre-trained on ImageNet-1k, featuring hierarchical feature maps and local window self-attention mechanisms with linear computational complexity.

Swin Transformer is a hierarchical vision Transformer model based on shifted windows, specifically designed for image classification tasks.

Swin Transformer is a hierarchical window-based vision Transformer model pretrained on the ImageNet-21k dataset, suitable for image classification tasks.

Swin Transformer is a hierarchical window-based vision Transformer model, pretrained on the ImageNet-21k dataset, suitable for image classification tasks.

Swin Transformer is a hierarchical vision Transformer based on shifted windows, specifically designed for image classification tasks.

Swin Transformer is a hierarchical window-based vision Transformer model designed for image classification tasks, with computational complexity linearly related to input image size.

Swin Transformer is a hierarchical vision Transformer that achieves linear computational complexity by computing self-attention within local windows, making it suitable for image classification tasks.

Swin Transformer is a hierarchical vision transformer based on shifted windows, suitable for image classification tasks.

Swin Transformer is a hierarchical vision Transformer that achieves linear computational complexity by computing self-attention within local windows, making it suitable for image classification and dense recognition tasks.

Swin Transformer is a hierarchical vision transformer based on shifted windows, pretrained on the ImageNet-21k dataset, suitable for image classification tasks.

Swin Transformer is a hierarchical vision transformer based on shifted windows, specifically designed for image classification tasks, with computational complexity linear to input image size.