DG-DETR: Toward Domain Generalized Detection Transformer

• URL: http://arxiv.org/abs/2504.19574v1
• Date: Mon, 28 Apr 2025 08:33:10 GMT
• Title: DG-DETR: Toward Domain Generalized Detection Transformer
• Authors: Seongmin Hwang, Daeyoung Han, Moongu Jeon,
• Abstract summary: We introduce a Domain Generalized DEtection TRansformer (DG-DETR) to enhance the robustness of Transformer-based detectors.<n>Specifically, we propose a novel domain-agnostic query selection strategy that removes domain-induced biases from object queries.<n> Experimental results validate the effectiveness of DG-DETR.
• Score: 8.762314897895175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: End-to-end Transformer-based detectors (DETRs) have demonstrated strong detection performance. However, domain generalization (DG) research has primarily focused on convolutional neural network (CNN)-based detectors, while paying little attention to enhancing the robustness of DETRs. In this letter, we introduce a Domain Generalized DEtection TRansformer (DG-DETR), a simple, effective, and plug-and-play method that improves out-of-distribution (OOD) robustness for DETRs. Specifically, we propose a novel domain-agnostic query selection strategy that removes domain-induced biases from object queries via orthogonal projection onto the instance-specific style space. Additionally, we leverage a wavelet decomposition to disentangle features into domain-invariant and domain-specific components, enabling synthesis of diverse latent styles while preserving the semantic features of objects. Experimental results validate the effectiveness of DG-DETR. Our code is available at https://github.com/sminhwang/DG-DETR.

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