Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification

Existing image augmentation methods consist of two categories: perturbation-based methods and generative methods. Perturbation-based methods apply pre-defined perturbations to augment an original image, but only locally vary the image, thus lacking image diversity. In contrast, generative methods bring more image diversity in the augmented images but may not preserve semantic consistency, thus may incorrectly change the essential semantics of the original image. To balance image diversity and semantic consistency in augmented images, we propose SGID, a Semantic-guided Generative Image augmentation method with Diffusion models for image classification. Specifically, SGID employs diffusion models to generate augmented images with good image diversity. More importantly, SGID takes image labels and captions as guidance to maintain semantic consistency between the augmented and original images. Experimental results show that SGID outperforms the best augmentation baseline by 1.72% on ResNet-50 (from scratch), 0.33% on ViT (ImageNet-21k), and 0.14% on CLIP-ViT (LAION-2B). Moreover, SGID can be combined with other image augmentation baselines and further improves the overall performance. We demonstrate the semantic consistency and image diversity of SGID through quantitative human and automated evaluations, as well as qualitative case studies.

Authors

• Bohan Li Shanghai Jiao Tong University, Shanghai, China Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China
• Xiao Xu Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology
• Xinghao Wang School of Computer Science, Fudan University
• Yutai Hou Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology
• Yunlong Feng Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology
• Feng Wang
• Xuanliang Zhang Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology
• Qingfu Zhu Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology
• Wanxiang Che Research Center for SCIR, Harbin Institute of Technology, Harbin, China

Keywords

• CV: Applications
• CV: Language and Vision
• General