Patch Representation

Patch representation in computer vision focuses on dividing images into smaller patches for processing, aiming to improve efficiency and performance in various tasks like image classification, segmentation, and generation. Current research emphasizes developing effective methods for extracting and utilizing patch-level features, often employing vision transformers (ViTs) and self-supervised learning techniques like contrastive learning and masked image modeling, alongside novel attention mechanisms and feature aggregation strategies. These advancements are driving improvements in accuracy and efficiency across diverse applications, including medical image analysis and high-resolution image synthesis, while also enabling more robust and explainable models.