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

• URL: http://arxiv.org/abs/2301.05499v1
• Date: Fri, 13 Jan 2023 12:01:18 GMT
• Title: CLIP the Gap: A Single Domain Generalization Approach for Object Detection
• Authors: Vidit Vidit, Martin Engilberge, Mathieu Salzmann
• Abstract summary: Single Domain Generalization tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. 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.
• Score: 60.20931827772482
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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