Transformer Based Framework

Transformer-based frameworks are rapidly advancing numerous fields by leveraging the power of self-attention mechanisms to process sequential and multi-modal data. Current research focuses on adapting transformer architectures, such as Vision Transformers and variations of BERT, to diverse applications including image processing, time series forecasting, and natural language processing tasks, often incorporating techniques like causal attention and novel loss functions to improve performance and efficiency. This approach is proving highly impactful, enabling advancements in areas like medical image analysis, traffic flow prediction, and anomaly detection through improved accuracy and reduced computational costs.