Single-image driven 3d viewpoint training data augmentation for
  effective wine label recognition
Yueh-Cheng Huang Hsin-Yi Chen Cheng-Jui Hung Jen-Hui Chuang Jenq-Neng Hwang
Confronting the critical challenge of insufficient training data in the field
of complex image recognition, this paper introduces a novel 3D viewpoint
augmentation technique specifically tailored for wine label recognition. This
method enhances deep learning model performance by generating visually
realistic training samples from a single real-world wine label image,
overcoming the challenges posed by the intricate combinations of text and
logos. Classical Generative Adversarial Network (GAN) methods fall short in
synthesizing such intricate content combination. Our proposed solution
leverages time-tested computer vision and image processing strategies to expand
our training dataset, thereby broadening the range of training samples for deep
learning applications. This innovative approach to data augmentation
circumvents the constraints of limited training resources. Using the augmented
training images through batch-all triplet metric learning on a Vision
Transformer (ViT) architecture, we can get the most discriminative embedding
features for every wine label, enabling us to perform one-shot recognition of
existing wine labels in the training classes or future newly collected wine
labels unavailable in the training. Experimental results show a significant
increase in recognition accuracy over conventional 2D data augmentation
techniques.