Hierarchical Vision Transformer

Hierarchical vision transformers (ViTs) aim to improve the efficiency and performance of standard vision transformers by processing images in a hierarchical manner, typically using local self-attention mechanisms to reduce computational complexity while preserving long-range dependencies. Current research focuses on optimizing these architectures for various tasks, including image classification, object detection, semantic segmentation, and medical image analysis, often employing techniques like masked image modeling and efficient pruning methods to enhance performance and reduce resource requirements. This approach offers significant potential for advancing computer vision applications by enabling the deployment of powerful transformer models on resource-constrained devices and improving accuracy in complex visual tasks.