InterAug: A Tuning-Free Augmentation Policy for Data-Efficient and Robust Object Detection

The recent progress in developing pre-trained models, trained on large-scale datasets, has highlighted the need for robust protocols to effectively adapt them to domain-specific data, especially when there is a limited amount of available data. Data augmentations can play a critical role in enabling data-efficient fine-tuning of pre-trained object detection models. Choosing the right augmentation policy for a given dataset is challenging and relies on knowledge about task-relevant invariances. In this work, we focus on an understudied aspect of this problem - can bounding box annotations be used to design more effective augmentation policies? Through InterAug, we make a critical finding that, we can leverage the annotations to infer the effective context for each object in a scene, as opposed to manipulating the entire scene or only within the prespecified bounding boxes. Using a rigorous empirical study with multiple benchmarks and architectures, we demonstrate the efficacy of InterAug in improving robustness and handling data scarcity. Finally, InterAug can be used with any off-the-shelf policy, does not require any modification to the architecture, and significantly outperforms existing protocols. Our codes can be found at https: //github.com/kowshikthopalli/InterAug.