Transformer Based Vision

Transformer-based vision leverages the attention mechanisms of transformer networks to process visual data, aiming to improve performance and efficiency in various computer vision tasks. Current research focuses on adapting transformer architectures, such as Vision Transformers (ViTs) and their variants (e.g., deformable transformers), for specific applications like autonomous driving, robotics, and medical image analysis, often incorporating techniques like Kalman filtering and low-rank adaptation for improved accuracy and efficiency. These advancements are significantly impacting fields ranging from robotics and autonomous systems to medical diagnostics, enabling more robust and accurate visual perception systems.