Token Transformer

Token Transformers represent a class of vision transformers aiming to improve efficiency and performance in various computer vision tasks by strategically managing the processing of image tokens. Current research focuses on developing architectures that selectively process only the most informative tokens, employing techniques like dynamic token passing, token sharing, and adaptive token allocation to reduce computational cost without significant accuracy loss. These advancements are leading to faster and more efficient models for applications such as semantic segmentation, depth estimation, and 3D object detection, particularly beneficial for resource-constrained environments like mobile robotics.