Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation
This addresses data scarcity issues for researchers and practitioners in fine-grained computer vision, though it is an incremental improvement over existing augmentation techniques.
The paper tackles the problem of limited training data in fine-grained classification tasks, such as rare animal identification, by introducing ALIA, a method that uses large vision and language models to automatically generate diverse image augmentations, resulting in improved performance over traditional augmentation and text-to-image methods.
Many fine-grained classification tasks, like rare animal identification, have limited training data and consequently classifiers trained on these datasets often fail to generalize to variations in the domain like changes in weather or location. As such, we explore how natural language descriptions of the domains seen in training data can be used with large vision models trained on diverse pretraining datasets to generate useful variations of the training data. We introduce ALIA (Automated Language-guided Image Augmentation), a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains and augment the training data via language-guided image editing. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. The resulting dataset is visually consistent with the original training data and offers significantly enhanced diversity. We show that ALIA is able to surpasses traditional data augmentation and text-to-image generated data on fine-grained classification tasks, including cases of domain generalization and contextual bias. Code is available at https://github.com/lisadunlap/ALIA.