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Hierarchical Vision Transformer using Shifted Windows

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taeseon ryu

Swin Transformer가 최근 오브젝트디텍션 그리고 Semantic segmentation분야에서의 성능이 가장 좋은 모델 중 하나로 주목 받고 있습니다. Swin Transformer nlp분야에서 많이 쓰이는 트랜스포머를 비전 분야에 적용한 모델로 Hierarchical feature maps과 Window-based Self-attention의 특징적입니다 Swin Transformer는 작년 구글에서 제안된 방법인 비전 트랜스포머의 한계점을 개선한 모델이라고 보시면 됩니다 트랜스포머의 한계란.. ㄷㄷ 이네요 이미지 처리팀의 김선옥님이 자세한 리뷰 도와주셨습니다!! 오늘도 많은 관심 미리 감사드립니다!! https://youtu.be/L3sH9tjkvKI

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Hierarchical Vision Transformer using Shifted Windows Submitted on 25 Mar 2021 Authors {Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo}@microsoft.com Swin Transformer: Image Processing Team Dawoon Heo / Jeongho Shin / Sanghyun Kim / Seonok Kim(🙋) 2021.05.23
2 Swin Transformer Why❓ Image classification: 🥇86.4 top-1 accuracy on ImageNet-1K Object detection: 🥇58.7 box AP and 51.1 🥇mask AP on COCO test-dev Semantic segmentation: 🥇53.5 mIoU on ADE20K val (1) https://paperswithcode.com/dataset/coco MS COCO Dataset Benchmarks (1)
3 Swin Transformer What❓ Hierarchical feature maps where at each level of hierarchy Self-attention is applied within local non- overlapping windows. Window-based Self-attention reduces the computational overhead. Swin Transformer is new vision Transformer with hierarchical feature maps and shifted windows. Vision Transformer (ViT) Swin Transformer
Introduction Swin Transformer Experiments Conclusion INDEX
Introduction
6 Swin Transformer Transformers & CNNs Attention can simultaneously extract all the information we need from the input and its inter-relation. couch A A cat sleeping on couch cat sleeping on Transformers CNNs CNNs are much more localized. It does not have the spa7al informa7on necessary for many tasks like instance recogni7on because convolu7ons don't consider distanced-pixels rela7ons.
7 Swin Transformer Self-attention Self-attention could be seen as a measurement of a specific word's effect on all other words of the same sentence. This same process can be applied to images calcula7ng the aAen7on of patches of the images and their rela7ons to each other. A cat sleeping on couch A cat sleeping on couch
8 Swin Transformer Motivation Challenges in adapting Transformer from language to vision arise from differences between the two domains. Regular self-attention requires quadratic of the image size number of operations, limiting applications in computer vision where high resolution is necessary. 250px 250px 62500 pixels 3906250000 calculations 🤯
Question 🤔
Swin Transformer
11 Swin Transformer Swin Transformer Atchitecture
12 Swin Transformer Patch Partition A cat sleeping on couch Token Token 8 8 Dimension = 48 (4x4x3 channels) At first, like most computer vision tasks, an RGB image is sent to the network. This image is split into patches, and each patch is treated as a token.
13 Swin Transformer Vision Transformer (ViT) vs Swin Transformer Vision Transformer (ViT) (2) Swin Transformer (2) An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (arXiv:2010.11929) ViT directly applies a Transformer architecture on non-overlapping image patches. It has the quadratic increase in complexity with image size. Swin transformer block modifies the multi-header self-attention.
14 Swin Transformer Swin Transformer Blocks W-MSA The input image is divided into four windows and compute attention for patches within the window. SW-MSA The approach introduces connections between neighboring non-overlapping windows in the previous layer.
15 Swin Transformer Shifted Window based Self-Attention Self-attention is applied on each patch, here referred to as windows. In layer l (left), a regular window partitioning scheme is adopted, and self-attention is computed within each window. Then, the windows are shifted, resulting in a new window configuration to apply self-attention again. This allows the creation of connections between windows while maintaining the computation efficiency of this windowed architecture.
16 Swin Transformer Cyclic-Shift The paper propose an efficient batch computation approach by cyclic-shifting toward the top-left direction After this shift, a batched window may be composed of several sub-windows that are not adjacent in the feature map, so a masking mechanism is employed to limit self-attention computation to within each sub- window.
Question 🤔
18 Swin Transformer Variants The paper also introduce Swin-T, Swin-S and Swin-L, which are versions of about 0.25x, 0.5x and 2x the model size and computational complexity, respectively. The value of c is from 96 to 128 and 192. The number of blocks is also changed at each stage from 6 to 18 or keeping them to 2
Experiments
20 Swin Transformer Ablation study The paper show the results of ablation study on shifted window approach. Speed of padding and cyclic method is also compared.
21 Swin Transformer Classification Results
22 Swin Transformer Detection Results
23 Swin Transformer Semantic Segmentation Results
Conclusion
25 Swin Transformer Conclusion Using a similar architecture for both NLP and computer vision could significantly accelerate the research process. A cat sleeping on couch A cat sleeping on couch This paper presents Swin Transformer, a new vision Transformer which produces a hierarchical feature representation and has linear computational complexity with respect to input image size. As a key element of Swin Transformer, the shifted window based self-attention is shown to be effective and efficient on vision problems.
Question 🤔
Thank you 😀
Sources Original Paper Paper (https://arxiv.org/pdf/2103.14030.pdf) GitHub (https://github.com/microsoft/Swin-Transformer) YouTube AI Bites (https://www.youtube.com/watch?v=tFYxJZBAbE8) What's AI (https://www.youtube.com/watch?v=QcCJJOLCeJQ&t=4s) Blog Hello Joo World! (https://velog.io/@hangjoo_-) Deep.log (https://velog.io/@yhyj1001) Seonok Kim (sokim0991@korea.ac.kr) Presenter

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Hierarchical Vision Transformer using Shifted Windows

  • 1. Hierarchical Vision Transformer using Shifted Windows Submitted on 25 Mar 2021 Authors {Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin and Baining Guo}@microsoft.com Swin Transformer: Image Processing Team Dawoon Heo / Jeongho Shin / Sanghyun Kim / Seonok Kim(🙋) 2021.05.23
  • 2. 2 Swin Transformer Why❓ Image classification: 🥇86.4 top-1 accuracy on ImageNet-1K Object detection: 🥇58.7 box AP and 51.1 🥇mask AP on COCO test-dev Semantic segmentation: 🥇53.5 mIoU on ADE20K val (1) https://paperswithcode.com/dataset/coco MS COCO Dataset Benchmarks (1)
  • 3. 3 Swin Transformer What❓ Hierarchical feature maps where at each level of hierarchy Self-attention is applied within local non- overlapping windows. Window-based Self-attention reduces the computational overhead. Swin Transformer is new vision Transformer with hierarchical feature maps and shifted windows. Vision Transformer (ViT) Swin Transformer
  • 6. 6 Swin Transformer Transformers & CNNs Attention can simultaneously extract all the information we need from the input and its inter-relation. couch A A cat sleeping on couch cat sleeping on Transformers CNNs CNNs are much more localized. It does not have the spa7al informa7on necessary for many tasks like instance recogni7on because convolu7ons don't consider distanced-pixels rela7ons.
  • 7. 7 Swin Transformer Self-attention Self-attention could be seen as a measurement of a specific word's effect on all other words of the same sentence. This same process can be applied to images calcula7ng the aAen7on of patches of the images and their rela7ons to each other. A cat sleeping on couch A cat sleeping on couch
  • 8. 8 Swin Transformer Motivation Challenges in adapting Transformer from language to vision arise from differences between the two domains. Regular self-attention requires quadratic of the image size number of operations, limiting applications in computer vision where high resolution is necessary. 250px 250px 62500 pixels 3906250000 calculations 🤯
  • 12. 12 Swin Transformer Patch Partition A cat sleeping on couch Token Token 8 8 Dimension = 48 (4x4x3 channels) At first, like most computer vision tasks, an RGB image is sent to the network. This image is split into patches, and each patch is treated as a token.
  • 13. 13 Swin Transformer Vision Transformer (ViT) vs Swin Transformer Vision Transformer (ViT) (2) Swin Transformer (2) An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (arXiv:2010.11929) ViT directly applies a Transformer architecture on non-overlapping image patches. It has the quadratic increase in complexity with image size. Swin transformer block modifies the multi-header self-attention.
  • 14. 14 Swin Transformer Swin Transformer Blocks W-MSA The input image is divided into four windows and compute attention for patches within the window. SW-MSA The approach introduces connections between neighboring non-overlapping windows in the previous layer.
  • 15. 15 Swin Transformer Shifted Window based Self-Attention Self-attention is applied on each patch, here referred to as windows. In layer l (left), a regular window partitioning scheme is adopted, and self-attention is computed within each window. Then, the windows are shifted, resulting in a new window configuration to apply self-attention again. This allows the creation of connections between windows while maintaining the computation efficiency of this windowed architecture.
  • 16. 16 Swin Transformer Cyclic-Shift The paper propose an efficient batch computation approach by cyclic-shifting toward the top-left direction After this shift, a batched window may be composed of several sub-windows that are not adjacent in the feature map, so a masking mechanism is employed to limit self-attention computation to within each sub- window.
  • 18. 18 Swin Transformer Variants The paper also introduce Swin-T, Swin-S and Swin-L, which are versions of about 0.25x, 0.5x and 2x the model size and computational complexity, respectively. The value of c is from 96 to 128 and 192. The number of blocks is also changed at each stage from 6 to 18 or keeping them to 2
  • 20. 20 Swin Transformer Ablation study The paper show the results of ablation study on shifted window approach. Speed of padding and cyclic method is also compared.
  • 25. 25 Swin Transformer Conclusion Using a similar architecture for both NLP and computer vision could significantly accelerate the research process. A cat sleeping on couch A cat sleeping on couch This paper presents Swin Transformer, a new vision Transformer which produces a hierarchical feature representation and has linear computational complexity with respect to input image size. As a key element of Swin Transformer, the shifted window based self-attention is shown to be effective and efficient on vision problems.
  • 28. Sources Original Paper Paper (https://arxiv.org/pdf/2103.14030.pdf) GitHub (https://github.com/microsoft/Swin-Transformer) YouTube AI Bites (https://www.youtube.com/watch?v=tFYxJZBAbE8) What's AI (https://www.youtube.com/watch?v=QcCJJOLCeJQ&t=4s) Blog Hello Joo World! (https://velog.io/@hangjoo_-) Deep.log (https://velog.io/@yhyj1001) Seonok Kim (sokim0991@korea.ac.kr) Presenter