Swin Transformer

Swin Transformer | findAIList | Find AI List

Overview

Swin Transformer is a hierarchical vision transformer designed as a general-purpose backbone for computer vision tasks. It employs a shifted windowing scheme to compute representations, limiting self-attention to non-overlapping local windows while enabling cross-window connections. This architecture offers greater efficiency and achieves strong performance in tasks like image classification, object detection, and semantic segmentation. The implementation supports various follow-up works including Video Swin Transformer for video action recognition, and SimMIM for masked image modeling based pre-training. It integrates with tools like FasterTransformer for optimized inference on Nvidia GPUs and Tutel for Mixture-of-Experts variants. The model allows feature distillation to improve fine-tuning performance across different pre-trained models.

Common tasks

Image Classification Object Detection Semantic Segmentation Video Action Recognition Self-Supervised Learning

FAQ

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What is Swin Transformer?

Swin Transformer is a hierarchical vision transformer that uses shifted windows to efficiently compute representations for various computer vision tasks.

What tasks does Swin Transformer support?

Swin Transformer supports image classification, object detection, semantic segmentation, video action recognition, and self-supervised learning.

How does Swin Transformer achieve efficiency?

Swin Transformer achieves efficiency by limiting self-attention computation to non-overlapping local windows and using shifted window partitioning.

What is SimMIM?

SimMIM is a masked image modeling based pre-training approach applicable to Swin Transformer, enabling the model to learn representations from unlabeled data.

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