CLIP the Gap: A Single Domain Generalization Approach for Object Detection

Abstract

Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remains almost non-existent. To address the challenges of simultaneously learning robust object localization and representation, we propose to leverage a pre-trained vision-language model to introduce semantic domain concepts via textual prompts. We achieve this via a semantic augmentation strategy acting on the features extracted by the detector backbone, as well as a text-based classification loss. Our experiments evidence the benefits of our approach, outperforming by 10% the only existing SDG object detection method, Single-DGOD [49], on their own diverse weather-driving benchmark.

Object-Aware Domain Generalization for Object Detection

“…However, the model does not diversify the singledomain with data augmentation, which potentially can be improved. Vidit et al (2023) utilized a pre-trained visionlanguage model to generalize a detector, but textual hints about the target domains should be given. Our proposed method does not require prior knowledge about target domains and achieves S-DGOD in an object-aware approach.…”