## 6 Hybrid 3D Convolutional-Transformer Model  
CNNs have demonstrated considerable success in applications such as image classification, object identification, and segmentation, significantly improving computer vision system performance and capabilities. In this respect, pre-trained temporal models based on CNN layers, used in computer vision, are models trained on large video datasets to grasp and capture spatial and temporal information. These models are designed to manage the intricate nature of video data, comprising static visual content inside individual frames (spatial information) and dynamic changes between frames over time (temporal information). These models have various advantages. First, they improve understanding because they can completely comprehend video information by considering both the visual aspects and their evolution over time. Second, transfer learning enables pre-trained models to be fine-tuned for certain tasks or datasets, increasing their adaptability while reducing computing resource requirements. Third, these models demonstrate greater performance in a variety of video-related tasks as compared to those that simply deal with spatial data. Finally, efficiency is significantly boosted since pre-trained models based on large-scale datasets speed up the development process by providing a strong fundamental framework. The next important leap in computer vision is represented by the Transformer architectures, which were originally developed for natural language processing (NLP). Then, the Vision Transformer (ViT) model has shown that it can outperform CNNs in a range of vision tasks by employing self-attention mechanisms. This allows them to better imitate long-range associations in images than CNNs, which are often limited by their receptive fields. Transformers have also enabled new applications and enhanced current ones. They excel at processing large-scale datasets and have been used for a variety of tasks, including picture production, video interpretation, and multi-modal learning, which combines visual information with other data types such as text. The shift to transformer-based models in computer vision represents a wider trend of integrating techniques across diverse disciplines of AI, resulting in more robust and adaptable systems. Pre-trained Temporal Vision Transformers (TimeSformer) offer a substantial leap in video interpretation tasks in computer vision. These models expand the capabilities of classic Vision Transformers (ViT) by processing the temporal information inherent in video data, allowing for a more thorough study of both spatial and temporal aspects. TimeSformers use the self-attention mechanism to detect relationships not just between individual frames but also across time. TimeSformers' capacity to represent temporal dynamics enables them to succeed in tasks like action identification, video categorization, and video segmentation. In particular, Temporal Pre-trained models are extremely helpful since they have been extensively trained on large-scale video datasets such as Kinetics or Moments in Time, allowing them to perform well even when task-specific data are sparse. Pre-trained Temporal Vision Transformers provide several key benefits, includingimproved performance through better temporal context understanding, efficient transfer learning that allows for fine-tuning on specific tasks with fewer resources, and versatility across multiple domains such as surveillance, autonomous driving, sports analytics, and healthcare. This progress offers a big step forward in linking spatial and temporal data interpretation, resulting in powerful tools for complete video analysis. The combination of a Temporal Vision Transformer (ViT) with a pre-trained temporal model improves video analysis by harnessing the capabilities of both designs. This hybrid technique enhances spatial and temporal feature extraction, leading to greater accuracy and resilience in tasks such as action identification and video segmentation.