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AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE

AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE

Reference: Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby
https://doi.org/10.48550/arXiv.2010.11929

Ref: https://doi.org/10.48550/arXiv.2010.11929

Issue:

The problem targeted in the paper is the limited application of the Transformer architecture in computer vision tasks. While the Transformer architecture has been widely adopted for natural language processing, its usage in computer vision has been constrained. Specifically, attention mechanisms are typically used alongside convolutional networks or to replace certain components of these networks while maintaining their overall structure. The paper aims to demonstrate that this reliance on convolutional neural networks (CNNs) is unnecessary and that a pure Transformer architecture applied directly to sequences of image patches can achieve excellent performance on image classification tasks.

Key Contribution:

The key contribution of the paper lies in introducing the Vision Transformer (ViT) model, which applies the Transformer architecture directly to sequences of image patches for image classification tasks. By pre-training the ViT model on large datasets and transferring it to various mid-sized or small image recognition benchmarks (such as ImageNet, CIFAR-100, and VTAB), the authors demonstrate that ViT achieves remarkable results compared to state-of-the-art convolutional networks while requiring fewer computational resources for training.

Outcome:

  • Demonstrating that ViT achieves excellent performance on image classification tasks when pre-trained on large datasets and transferred to multiple benchmarks.
  • Analyzing the usage of self-attention in ViT through metrics such as attention distance and attention maps, providing insights into how ViT integrates information across images.
  • Reporting results on the ObjectNet benchmark, where the ViT-H/14 model achieves 82.1% top-5 accuracy and 61.7% top-1 accuracy.
  • Presenting scores attained on various tasks within the VTAB-1k benchmark, showcasing the performance of ViT across different image recognition tasks.
Ref: https://doi.org/10.48550/arXiv.2010.11929

Summary:

The paper addresses the limitation of the Transformer architecture in computer vision tasks by introducing the Vision Transformer (ViT) model, which applies the Transformer directly to sequences of image patches for image classification. Through extensive experiments on various benchmarks, including ImageNet, CIFAR-100, VTAB, ObjectNet, and VTAB-1k tasks, the authors demonstrate that ViT achieves excellent performance compared to state-of-the-art convolutional networks while requiring fewer computational resources. Additionally, the paper provides insights into how ViT utilizes self-attention through analyses of attention distance and attention maps, further enhancing our understanding of its functioning in image recognition tasks.

Ref: https://doi.org/10.48550/arXiv.2010.11929

Ref: https://doi.org/10.48550/arXiv.2010.11929

Pratham Goyal

Bachelors of Technology | Experienced data scientist and DL engineer adept in ML, DL, with 30+ projects, including research. Master @Kaggle with a global rank within 60 in datasets

Technical Skills:
Programming Languages: C, C++, Python, Java
Tools: Docker, GitHub, Oops, HTML, CSS, Flask, Node.js, FastAPI , End-to-End Cl/CD Pipeline

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