An Overview of Vision Transformers for Image Processing: A Survey

Using image processing technology has become increasingly essential in the education sector, with universities and educational institutions exploring innovative ways to enhance their teaching techniques and provide a better learning experience for their students. Vision transformer-based models have been highly successful in various domains of artificial intelligence, including natural language processing and computer vision, which have generated significant interest from academic and industrial researchers. These models have outperformed other networks like convolutional and recurrent networks in visual benchmarks, making them a promising candidate for image processing applications. This article presents a comprehensive survey of vision transformer models for image processing and computer vision, focusing on their potential applications for student verification in university systems. The models can analyze biometric data like student ID cards and facial recognition to ensure that students are accurately verified in real-time, becoming increasingly vital as online learning continues to gain traction. By accurately verifying the identity of students, universities and educational institutions can guarantee that students have access to relevant learning materials and resources necessary for their academic success.

@article{Kameswari2023,
  title     = {An Overview of Vision Transformers for Image Processing: A Survey},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Ch. Sita Kameswari and Kavitha J and T. Srinivas Reddy and Balaswamy Chinthaguntla and Senthil Kumar Jagatheesaperumal and Silvia Gaftandzhieva and Rositsa Doneva},
  doi       = {10.14569/IJACSA.2023.0140830},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140830}
}