Abstract
Tracklet quality is often treated as an afterthought in most person re-identification (ReID) methods, with the majority of research presenting architectural modifications to foundational models. Such approaches neglect an important limitation, posing challenges when deploying ReID systems in real-world, difficult scenarios. In this paper, we introduce S3-CLIP, a video super-resolution-based CLIP-ReID framework developed for the VReID-XFD challenge at WACV 2026. The proposed method integrates recent advances in super-resolution networks with task-driven super-resolution pipelines, adapting them to the video-based person re-identification setting. To the best of our knowledge, this work represents the first systematic investigation of video super-resolution as a means of enhancing tracklet quality for person ReID, particularly under challenging cross-view conditions. Experimental results demonstrate performance competitive with the baseline, achieving 37.52% mAP in aerial-to-ground and 29.16% mAP in ground-to-aerial scenarios. In the ground-to-aerial setting, S3-CLIP achieves substantial gains in ranking accuracy, improving Rank-1, Rank-5, and Rank-10 performance by 11.24%, 13.48%, and 17.98%, respectively.
Abstract (translated)
在大多数人物重新识别（ReID）方法中，轨迹质量通常被当作次要问题处理，而多数研究集中在对基础模型进行架构上的改进。这种做法忽视了一个重要的局限性，在实际部署ReID系统时会遇到挑战，尤其是在复杂且困难的场景下。在这篇论文中，我们介绍了S3-CLIP框架，这是一个基于视频超分辨率（Video Super-Resolution）和CLIP-ReID技术相结合的方法，并用于WACV 2026年举办的VReID-XFD竞赛。该方法将最近在超分辨率网络中的进展与任务驱动的超分辨率流程集成起来，适应于基于视频的人物重新识别场景。 据我们所知，这项工作是首次系统性地研究视频超分辨率技术如何提升轨迹质量以增强人物ReID性能的研究，特别是在跨视角（cross-view）条件下。实验结果表明，我们的方法在性能上与基准线相当，并且在从空中到地面和从地面到空中的场景中分别达到了37.52%的mAP和29.16%的mAP。尤其是在地面到空中设置的情况下，S3-CLIP框架显著提高了排名准确度，在Rank-1、Rank-5和Rank-10性能方面分别提升了11.24%，13.48%，以及17.98%。
URL
https://arxiv.org/abs/2601.08807
