Abstract
In surveillance scenarios, varying camera distances cause significant differences among pedestrian image resolutions, making it hard to match low-resolution (LR) images with high-resolution (HR) counterparts, limiting the performance of Re-Identification (ReID) tasks. Most existing Cross-Resolution ReID (CR-ReID) methods rely on super-resolution (SR) or joint learning for feature compensation, which increases training and inference complexity and has reached a performance bottleneck in recent studies. Inspired by semantic directions in the word embedding space, we empirically discover that semantic directions implying resolution differences also emerge in the feature space of ReID, and we substantiate this finding from a statistical perspective using Canonical Correlation Analysis and Pearson Correlation Analysis. Based on this interesting finding, we propose a lightweight and effective Vector Panning Feature Alignment (VPFA) framework, which conducts CR-ReID from a novel perspective of modeling the resolution-specific feature discrepancy. Extensive experimental results on multiple CR-ReID benchmarks show that our method significantly outperforms previous state-of-the-art baseline models while obtaining higher efficiency, demonstrating the effectiveness and superiority of our model based on the new finding in this paper.
Abstract (translated)
在监控场景中，不同的摄像机距离导致行人图像分辨率存在显著差异，使得低分辨率（LR）图像与高分辨率（HR）图像的匹配变得困难，从而限制了重识别（ReID）任务的表现。大多数现有的跨分辨率重识别（CR-ReID）方法依赖于超分辨率（SR）或联合学习来进行特征补偿，这增加了训练和推理的复杂性，并且在最近的研究中已达到性能瓶颈。 受到词嵌入空间中语义方向的启发，我们通过实验发现，在ReID的特征空间中也存在表示分辨率差异的语义方向。利用典型相关分析（CCA）和皮尔逊相关分析从统计学角度证实了这一发现。基于这一有趣的发现，我们提出了一种轻量级且有效的向量平移特征对齐（VPFA）框架，该框架以一种新颖的角度来建模分辨率特异性的特征差异，并以此来进行跨分辨率重识别。 在多个CR-ReID基准测试上的广泛实验结果表明，我们的方法显著优于之前的最先进基线模型，并获得了更高的效率，证明了基于本文新发现的模型的有效性和优越性。
URL
https://arxiv.org/abs/2510.00936
