Random Crop

Random cropping, a common data augmentation technique in computer vision, involves randomly selecting a portion of an image for training. Current research focuses on improving the effectiveness of random cropping, particularly by addressing issues like semantic inconsistency in complex scenes and the need for extensive training epochs. This is being tackled through methods that incorporate contrastive learning, segmentation information, and techniques to control the similarity of representations under different crops. Improved random cropping strategies lead to more efficient and robust deep learning models across various applications, including image classification, object detection, and video analysis.