标题： SCING：通过选择性跨模态提示微调实现更高效和鲁棒的人体再识别 显示英文标题

标题： SCING:Towards More Efficient and Robust Person Re-Identification through Selective Cross-modal Prompt Tuning

摘要： 最近在将像CLIP这样的视觉-语言预训练模型适应于人员重识别（ReID）任务方面的进展，通常依赖于复杂的适配器设计或模态特定的调整，而忽视了跨模态交互，导致计算成本高或对齐效果不佳。 为了解决这些限制，我们提出了一种简单但有效的框架，名为选择性跨模态提示调优（SCING），该框架增强了跨模态对齐能力和对现实世界扰动的鲁棒性。 我们的方法引入了两个关键创新：首先，我们提出了选择性视觉提示融合（SVIP），这是一个轻量级模块，通过跨模态门控机制动态地将区分性视觉特征注入文本提示中。 此外，所提出的扰动驱动一致性对齐（PDCA）是一种双路径训练策略，通过正则化原始和增强的跨模态嵌入之间的一致性，在随机图像扰动下强制不变特征对齐。 在几个流行的基准测试上进行了广泛的实验，包括Market1501、DukeMTMC-ReID、Occluded-Duke、Occluded-REID和P-DukeMTMC，结果表明了所提方法的出色性能。 值得注意的是，我们的框架消除了重型适配器，同时保持了高效的推理，实现了性能与计算开销之间的最佳平衡。 代码将在接受后发布。 显示英文摘要

摘要： Recent advancements in adapting vision-language pre-training models like CLIP for person re-identification (ReID) tasks often rely on complex adapter design or modality-specific tuning while neglecting cross-modal interaction, leading to high computational costs or suboptimal alignment. To address these limitations, we propose a simple yet effective framework named Selective Cross-modal Prompt Tuning (SCING) that enhances cross-modal alignment and robustness against real-world perturbations. Our method introduces two key innovations: Firstly, we proposed Selective Visual Prompt Fusion (SVIP), a lightweight module that dynamically injects discriminative visual features into text prompts via a cross-modal gating mechanism. Moreover, the proposed Perturbation-Driven Consistency Alignment (PDCA) is a dual-path training strategy that enforces invariant feature alignment under random image perturbations by regularizing consistency between original and augmented cross-modal embeddings. Extensive experiments are conducted on several popular benchmarks covering Market1501, DukeMTMC-ReID, Occluded-Duke, Occluded-REID, and P-DukeMTMC, which demonstrate the impressive performance of the proposed method. Notably, our framework eliminates heavy adapters while maintaining efficient inference, achieving an optimal trade-off between performance and computational overhead. The code will be released upon acceptance.