提出自感知对齐机制，提升交通场景跨域目标检测精度
Self-Aware Adaptive Alignment: Enabling Accurate Perception for Intelligent Transportation Systems
- 用注意力模块实现源域与目标域的局部全局自适应对齐
- 在多个跨域检测数据集上超越现有最佳方法
- 适合需要跨域泛化的智能交通系统开发者参考
智能交通检测性能的提升是重要研究方向，但跨域场景下的检测仍面临诸多挑战。本文提出自感知自适应对齐（SA3）方法，通过高效的对齐机制与识别策略，利用在源域和目标域数据集上训练的注意力对齐模块，引导图像级特征对齐，实现源域与目标域之间的局部-全局自适应对齐。两个域的特征经通道重要性重加权后输入区域建议网络，以获取显著区域特征。此外，引入针对目标域的实例-图像级对齐模块，自适应缓解域间差异。在多个流行的跨域目标检测基准上进行大量实验，结果表明SA3优于先前最先进方法。
原文摘要 · Abstract (English)
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.
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