Abstract
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. On fine-grained and cluttered datasets for classification and detection, ALIA surpasses traditional data augmentation and text-to-image generated data by up to 15\%, often even outperforming equivalent additions of real data. Code is avilable at this https URL.
Abstract (translated)
许多精细的分类任务，例如稀有动物识别，训练数据有限，因此训练在这些数据上的分类器往往无法泛化到domain的变化，例如天气或地点的变化。因此，我们探索如何使用训练数据中的自然语言描述生成大型视觉模型，通过语言引导的图像编辑来生成有用的训练数据变异。我们引入了ALIA(自动语言引导图像增强)，这种方法利用大型视觉和语言模型自动生成dataset的domain的自然语言描述，并通过语言引导的图像编辑增强训练数据。为了维持数据完整性，训练在原始数据上的分类器过滤掉最小图像编辑和那些损坏类相关的信息。 resulting dataset与原始训练数据 visually consistent，并提供了显著的增加多样性。在精细的分类和检测数据集上，ALIA超过了传统的数据增强和文本到图像生成的数据，可以达到15\%以上的超越，常常甚至超越了真实的数据增加。代码在这个httpsURL上可用。
URL
https://arxiv.org/abs/2305.16289
