Boosting Single-domain Generalized Object Detection via Vision-Language Knowledge Interaction

• URL: http://arxiv.org/abs/2504.19086v1
• Date: Sun, 27 Apr 2025 02:55:54 GMT
• Title: Boosting Single-domain Generalized Object Detection via Vision-Language Knowledge Interaction
• Authors: Xiaoran Xu, Jiangang Yang, Wenyue Chong, Wenhui Shi, Shichu Sun, Jing Xing, Jian Liu,
• Abstract summary: Single-Domain Generalized Object Detection(S-DGOD) aims to train an object detector on a single source domain.<n>Recent S-DGOD approaches exploit pre-trained vision-language knowledge to guide invariant feature learning across visual domains.<n>We propose a new cross-modal feature learning method, which can capture generalized and discriminative regional features for S-DGOD tasks.
• Score: 4.692621855184482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single-Domain Generalized Object Detection~(S-DGOD) aims to train an object detector on a single source domain while generalizing well to diverse unseen target domains, making it suitable for multimedia applications that involve various domain shifts, such as intelligent video surveillance and VR/AR technologies. With the success of large-scale Vision-Language Models, recent S-DGOD approaches exploit pre-trained vision-language knowledge to guide invariant feature learning across visual domains. However, the utilized knowledge remains at a coarse-grained level~(e.g., the textual description of adverse weather paired with the image) and serves as an implicit regularization for guidance, struggling to learn accurate region- and object-level features in varying domains. In this work, we propose a new cross-modal feature learning method, which can capture generalized and discriminative regional features for S-DGOD tasks. The core of our method is the mechanism of Cross-modal and Region-aware Feature Interaction, which simultaneously learns both inter-modal and intra-modal regional invariance through dynamic interactions between fine-grained textual and visual features. Moreover, we design a simple but effective strategy called Cross-domain Proposal Refining and Mixing, which aligns the position of region proposals across multiple domains and diversifies them, enhancing the localization ability of detectors in unseen scenarios. Our method achieves new state-of-the-art results on S-DGOD benchmark datasets, with improvements of +8.8\%~mPC on Cityscapes-C and +7.9\%~mPC on DWD over baselines, demonstrating its efficacy.

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