# Vision Transformers and Transfer Learning Approaches for Arabic Sign Language Recognition
## 3 Methodology
### Transfer Learning for ArSL Classification  
Image classification is the process of learning features from images and categorizing them according to these features. Throughout this section, different pretrained small- and large-size deep learning models for image classification were utilized. By fine-tuning these models, they learned an inner representation of images that could then be used to extract features useful for downstream tasks. In our research, an ArSL dataset of labeled images were used to train a standard classifier by placing a linear layer on top of the pretrained models. We utilized the transfer learning approach in our experiments, following a series of stages: preprocessing the data, dividing it into segments, fine-tuning the hyperparameters, determining the loss function and optimizer, defining the model architecture, and training it. Once trained, we validated and tested the results. Several deep learning pretrained models were utilized in this study as follows. VGG was introduced by the Oxford Vision Group and achieved second place in the ImageNet challenge [67]. The VGG family of networks remains popular today and is often used as a benchmark for comparing newer architectures. VGG improved upon previous designs by replacing a single 5 \(\times\) 5 layer with two stacked 3 \(\times\) 3 layers, or a single  
Figure 1: The general framework of the methodology proposed for this study. \(7\times 7\) layer with three stacked \(3\times 3\) layers. This approach has several benefits, as using stacked layers reduces the number of weights and operations compared to a single layer with a large filter size. Moreover, stacking multiple layers enhances the discriminative capabilities of the decision function. We utilized in this study two variants: VGG16, as one of the baselines in [39], and VGG19. ResNet, short for residual neural network, is a deep learning architecture that was introduced by researchers at Microsoft [68]. It has become one of the most popular and influential network architectures in the field of computer vision. The main idea behind ResNet is the use of residual connections, which aim to overcome the degradation problem that arises when deep neural networks are trained. The degradation problem refers to the observation that as the network depth increases, the network's accuracy starts to saturate and then rapidly degrade. To address this problem, ResNet introduces \"skip connections\" that allow the network to bypass some layers and learn residual mappings. These skip connections connect layers that are not adjacent to each other, allowing the network to skip over certain layers and reuse the learned features from previous layers. By doing so, ResNet ensures that the gradient signals during the training process can flow more easily, preventing the degradation problem. The main building block in ResNet is called a residual block. It consists of two or more convolutional layers, usually with a size of \(3\times 3\), and a skip connection that adds the input of the block to the output of the block. This skip connection enables the network to learn the residual mapping, which is the difference between the input and the output of the block. ResNet-152, as another baseline in [40], and ResNet-50, with the number indicating the total number of layers in the network, were used in this study. MobileNet [69] and MobileNetV2 [70] were also utilized in this study. MobileNet is a convolutional neural network architecture designed to be lightweight and efficient for mobile and embedded devices with limited computational resources. MobileNet achieves this efficiency by using a combination of depthwise separable convolutions and a technique called linear bottlenecks. Depthwise separable convolutions split the standard convolution operation into two separate operations: a depthwise convolution and a pointwise convolution. This reduces the number of parameters and computations required, resulting in a lighter model. Both variants were applied to the ArSL classification dataset used in our study.