Table 1 Insight into related recent research.

“An Image is Worth 16 × 16 Words: Transformers for Image Recognition at Scale”⁵³

Each segmented patch is linearly projected into a high-dimensional embedding space that result is then input into the Transformer encoder.They replaced the traditional CNN backbone with a Transformer encoder-decoder framework, thereby enabling a more unified framework across modalities obtained cutting-edge performance on benchmark datasets with fewer computational resources than traditional CNN-based methods

results can be improved by adjusting the number of layers, the dimensionality of the embeddings, or the design of the attention mechanism and by fine-tuning the architecture to strike a balance between model capacity and computational efficiency

Transformers demand more processing power and memory than convolutional neural networks (CNNs), and the article does not elucidate how to address this. Transformers are less interpretable than CNNs, and interpretability strategies are not discussed in the article. Patch size, computational efficiency, and performance compromises are not considered. Resolving these issues could facilitate the scalability of image recognition methods based on the Transformer

“Show, attend and tell: Neural image caption generation with visual attention”¹⁷

The authors demonstrate the effectiveness of incorporating a visual attention mechanism into the caption generation process. The attention mechanism allows the model to focus on various portions of the image while generating each word in the caption, thereby improving the alignment between the image content and the generated text

superior caption quality in comparison to previous methods. By focusing on pertinent image regions, the model generates more accurate and descriptive captions that capture the image's most important objects, actions, and relationships

The approach lacks fine-grained attention because it employs a mechanism for soft attention that assigns weights to image regions rather than concentrating on particular objects or attributes. This hinders the capability of the model to generate captions with precise details. The article does not discuss strategies or techniques for fine-tuning the interpretation and control of the attention mechanism, thereby limiting the adaptability and interpretability

“Deep MRI Reconstruction with Generative Vision Transformers”⁵⁴

Deep generative network GVTrans translates noisy variables and latent onto high-quality MR images. Multi-layer architecture improves image resolution. Cross-attention transformer modules receive up-sampled feature maps in each layer. MR images are masked using the same sampling pattern as the under-sampled acquisition for test data inference. Optimized network parameters ensure that reconstructed and original k-space samples match

better image quality than CNN-based reconstructions with and without self-attention processes and can adjust to individual test subjects. GVTrans may improve deep MRI reconstruction applicability and generalizability

Using a larger dataset of fully-sampled MRI acquisitions for training GVTrans, incorporating additional information, such as patient demographics or clinical history, into the training process, and developing a more efficient training algorithm for GVTrans can improve the performance of the proposed architecture GVTrans.Training in the proposed GVTrans architecture is computationally intensive.GVTrans may be unable to reconstruct images with high levels of noise or anomalies, as well as images with very low sampling rates

“A Simple Single-Scale Vision Transformer for Object Detection and Instance Segmentation”⁴⁶

Universal Vision Transformer (UViT), an intuitive and efficient Vision Transformer architecture, was proposed for object detection and instance segmentation

UViT is a simple yet efficient model that achieves competitive performance on the COCO benchmarks for object detection and instance segmentation

On some tasks, such as dense prediction, UViT may not attain the same level of performance as more complex Vision Transformer architectures. UViT may not be as effective as models for object detection and instance segmentation that are more specialized

“Training data-efficient image transformers & distillation through attention”³⁴

A large, pre-trained convolutional neural network (CNN) is used as a teacher to train a smaller, more efficient transformer-based student model in this method. The student model gains knowledge from the teacher by observing the instructor's output, which is represented by a distillation token. The distillation token is added to the input of the student model and is utilized to direct the attention mechanism

DeiT-B model obtains 85.2% top-1 accuracy on ImageNet with 86 M-parameterwhen trained with 100 epochs and 16 GPUs

The distillation token can be computationally expensive to compute, which is a limitation. Another limitation is that the distillation token can result in a reduction in the attention weights' diversity.It would be possible to enhance the distillation token by employing a more efficient method for computing it. The distillation token could be modified to promote attention weights with greater diversity. The method could be applied to additional tasks, including object detection and segmentation

“Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography”⁵⁵

utilizing a standard Vision Transformer architecture and training it on a large collection of natural images. Using a limited number of labeled examples, they then refined this model using the CheXpert or Pediatric Pneumonia dataset

A model's performance on a medical image classification task can be considerably enhanced by transfer learning from a previously trained Vision Transformer. There is no significant effect on the efficacy of the model by fine-tuning

Domain adaption and other transfer learning methods may improve Vision Transformers' medical image classification performance in future research. The model’s performance can further be improved using larger fine-tuning datasets

“Introducing Convolutions to Vision Transformers”⁵⁶

a novel design called Convolutional Vision Transformer (CvT) that increases Vision Transformers (ViTs) performance and efficiency by adding convolutions.A convolutional token embedding layer replaces the token embedding layer. This enables the CVT to discover spatial relationships between tokens, thereby enhancing the model's capacity to represent complex visual patterns. Convolutional attention operation replaces the attention operation. This enables the CvT to efficiently compute attention weights across vast spatial regions, thereby enhancing the model's capacity to capture global context

CvT outperforms ViTs on a variety of image classification tasks while requiring fewer parameters and FLOPs. For instance, the CvT achieves a top-1 accuracy of 89.4% on the ImageNet-1 k dataset, which is comparable to the state-of-the-art performance of ResNet-50 despite employing only 1/10th of the parameters and 1/100th of the FLOPs

CvTs are harder to train and slower at inference hen compared with ViT's. Using deeper and broader CvT models to further improve performance, adding residual connections between CvT layers to improve training stability, and employing dilated convolutions and group convolutions to improve the model's ability to represent long-range dependencies can further improve the proposed model