Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918127252537344 |
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| author | Xiang, Tong Zhao, Hongxia Zhu, Fenghua Chen, Yuanyuan Lv, Yisheng |
| author_facet | Xiang, Tong Zhao, Hongxia Zhu, Fenghua Chen, Yuanyuan Lv, Yisheng |
| contents | Achieving top-notch performance in Intelligent Transportation detection is a critical research area. However, many challenges still need to be addressed when it comes to detecting in a cross-domain scenario. In this paper, we propose a Self-Aware Adaptive Alignment (SA3), by leveraging an efficient alignment mechanism and recognition strategy. Our proposed method employs a specified attention-based alignment module trained on source and target domain datasets to guide the image-level features alignment process, enabling the local-global adaptive alignment between the source domain and target domain. Features from both domains, whose channel importance is re-weighted, are fed into the region proposal network, which facilitates the acquisition of salient region features. Also, we introduce an instance-to-image level alignment module specific to the target domain to adaptively mitigate the domain gap. To evaluate the proposed method, extensive experiments have been conducted on popular cross-domain object detection benchmarks. Experimental results show that SA3 achieves superior results to the previous state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_13823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems Xiang, Tong Zhao, Hongxia Zhu, Fenghua Chen, Yuanyuan Lv, Yisheng Computer Vision and Pattern Recognition Achieving top-notch performance in Intelligent Transportation detection is a critical research area. However, many challenges still need to be addressed when it comes to detecting in a cross-domain scenario. In this paper, we propose a Self-Aware Adaptive Alignment (SA3), by leveraging an efficient alignment mechanism and recognition strategy. Our proposed method employs a specified attention-based alignment module trained on source and target domain datasets to guide the image-level features alignment process, enabling the local-global adaptive alignment between the source domain and target domain. Features from both domains, whose channel importance is re-weighted, are fed into the region proposal network, which facilitates the acquisition of salient region features. Also, we introduce an instance-to-image level alignment module specific to the target domain to adaptively mitigate the domain gap. To evaluate the proposed method, extensive experiments have been conducted on popular cross-domain object detection benchmarks. Experimental results show that SA3 achieves superior results to the previous state-of-the-art methods. |
| title | Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.13823 |