Vision Transformer: Vit and its Derivatives
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Abstract (arXiv)
Transformer, an attention-based encoder-decoder architecture, has not only revolutionized the field of natural language processing (NLP), but has also done some pioneering work in the field of computer vision (CV). Compared to convolutional neural networks (CNNs), the Vision Transformer (ViT) relies on excellent modeling capabilities to achieve very good performance on several benchmarks such as ImageNet, COCO, and ADE20k. ViT is inspired by the self-attention mechanism in natural language processing, where word embeddings are replaced with patch embeddings. This paper reviews the derivatives of ViT and the cross-applications of ViT with other fields.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 11 of its 12 references that are in the map. First posted on arXiv 12 May 2022.
Builds on
11 of 12 references.
References · 11
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
- 2021Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionscited by 1,960
- 2021Is Space-Time Attention All You Need for Video Understanding?cited by 1,609
- 2020Linformer: Self-Attention with Linear Complexitycited by 1,462
- 2021MLP-Mixer: An all-MLP Architecture for Visioncited by 1,441
- 2021Scaling Vision Transformerscited by 993
- 2021UNETR: Transformers for 3D Medical Image Segmentationcited by 954
- 2021Multiscale Vision Transformerscited by 896
- 2021PVT v2: Improved Baselines with Pyramid Vision Transformercited by 749
- 2021XCiT: Cross-Covariance Image Transformerscited by 335
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.