Abstract
Re-Identification (ReID) is a critical technology in intelligent perception systems, especially within autonomous driving, where onboard cameras must identify pedestrians across views and time in real-time to support safe navigation and trajectory prediction. However, the presence of uncertain or missing input modalities--such as RGB, infrared, sketches, or textual descriptions--poses significant challenges to conventional ReID approaches. While large-scale pre-trained models offer strong multimodal semantic modeling capabilities, their computational overhead limits practical deployment in resource-constrained environments. To address these challenges, we propose a lightweight Uncertainty Modal Modeling (UMM) framework, which integrates a multimodal token mapper, synthetic modality augmentation strategy, and cross-modal cue interactive learner. Together, these components enable unified feature representation, mitigate the impact of missing modalities, and extract complementary information across different data types. Additionally, UMM leverages CLIP's vision-language alignment ability to fuse multimodal inputs efficiently without extensive finetuning. Experimental results demonstrate that UMM achieves strong robustness, generalization, and computational efficiency under uncertain modality conditions, offering a scalable and practical solution for pedestrian re-identification in autonomous driving scenarios.
Abstract (translated)
重新识别（ReID）技术在智能感知系统中至关重要，尤其是在自主驾驶领域。车载摄像头需要实时识别不同视角和时间点上的行人，以支持安全导航和轨迹预测。然而，不确定或缺失的输入模态——例如RGB图像、红外图像、素描图或文本描述——对传统ReID方法构成了重大挑战。虽然大规模预训练模型提供了强大的多模态语义建模能力，但其计算开销限制了在资源受限环境中的实际部署。 为了解决这些问题，我们提出了一种轻量级的不确定性模式建模（UMM）框架，该框架整合了一个多模态令牌映射器、合成模态增强策略和跨模态线索互动学习者。这些组件共同作用，使得在不同数据类型间能够统一特征表示、减轻缺失模态的影响并提取互补信息成为可能。此外，UMM利用CLIP的视觉-语言对齐能力来高效融合多模态输入，无需进行大量微调。 实验结果表明，在不确定模态条件下，UMM框架具备强大的鲁棒性、泛化能力和计算效率，为自主驾驶场景中的行人重新识别提供了一个可扩展且实用的解决方案。
URL
https://arxiv.org/abs/2508.11218
