Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection

• URL: http://arxiv.org/abs/2402.01304v2
• Date: Mon, 5 Feb 2024 03:04:58 GMT
• Title: Phrase Grounding-based Style Transfer for Single-Domain Generalized Object Detection
• Authors: Hao Li, Wei Wang, Cong Wang, Zhigang Luo, Xinwang Liu, Kenli Li and Xiaochun Cao
• Abstract summary: Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains. This is a practical yet challenging task as it requires the model to address domain shift without incorporating target domain data into training. We propose a novel phrase grounding-based style transfer approach for the task.
• Score: 109.58348694132091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-domain generalized object detection aims to enhance a model's generalizability to multiple unseen target domains using only data from a single source domain during training. This is a practical yet challenging task as it requires the model to address domain shift without incorporating target domain data into training. In this paper, we propose a novel phrase grounding-based style transfer (PGST) approach for the task. Specifically, we first define textual prompts to describe potential objects for each unseen target domain. Then, we leverage the grounded language-image pre-training (GLIP) model to learn the style of these target domains and achieve style transfer from the source to the target domain. The style-transferred source visual features are semantically rich and could be close to imaginary counterparts in the target domain. Finally, we employ these style-transferred visual features to fine-tune GLIP. By introducing imaginary counterparts, the detector could be effectively generalized to unseen target domains using only a single source domain for training. Extensive experimental results on five diverse weather driving benchmarks demonstrate our proposed approach achieves state-of-the-art performance, even surpassing some domain adaptive methods that incorporate target domain images into the training process.The source codes and pre-trained models will be made available.

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