Focal Transformer

Focal Transformers represent a class of vision and language transformers designed to improve efficiency and accuracy by selectively focusing attention on the most relevant parts of the input data. Current research emphasizes developing novel architectures, such as those incorporating Gabor filters or multi-scale token aggregation, to reduce computational costs and enhance performance on tasks like image classification, object detection, and segmentation, particularly with limited data. These advancements are significant because they address limitations of standard transformers in handling high-resolution images and long contexts, leading to more efficient and effective models for various applications.