Enhancing Computer Vision Using Vision Transformers and Hybrid CNN-Transformer Architectures

Originally designed for natural language processing, the transformer mostly depends on deep neural networks' self-attention techniques. Researchers are now looking into using it for tasks associated with computer vision because of its robust representation abilities. In several visual benchmarks, transformer-based models have matched or exceeded the performance of convolutional and recurrent neural networks. Due to its reduced dependence on vision-specific inductive biases, this work has excited the curiosity of the computer vision field. The study examines several vision transformer models, contrasting and evaluating them according to four categories: video processing, high/mid-level vision tasks, low-level vision tasks, and backbone networks. There is also a discussion of effective transformer techniques for real-world implementation. The basic importance of the self-attention process in transformers used in computer vision is also discussed in this work.