Class Relevant Patch

Class-relevant patch processing is a burgeoning area of computer vision research focused on efficiently and effectively utilizing image patches – small, localized regions of an image – for various tasks. Current research explores optimizing patch selection and ranking for improved model efficiency and robustness, often employing Vision Transformers (ViTs) and incorporating techniques like contrastive learning and graph neural networks to enhance feature extraction and representation learning. This work holds significant implications for improving the speed and accuracy of image classification, object detection, and other vision applications, particularly in resource-constrained environments or when dealing with limited data.