Abstract
Current visible-infrared cross-modality person re-identification research has only focused on exploring the bi-modality mutual retrieval paradigm, and we propose a new and more practical mix-modality retrieval paradigm. Existing Visible-Infrared person re-identification (VI-ReID) methods have achieved some results in the bi-modality mutual retrieval paradigm by learning the correspondence between visible and infrared modalities. However, significant performance degradation occurs due to the modality confusion problem when these methods are applied to the new mix-modality paradigm. Therefore, this paper proposes a Mix-Modality person re-identification (MM-ReID) task, explores the influence of modality mixing ratio on performance, and constructs mix-modality test sets for existing datasets according to the new mix-modality testing paradigm. To solve the modality confusion problem in MM-ReID, we propose a Cross-Identity Discrimination Harmonization Loss (CIDHL) adjusting the distribution of samples in the hyperspherical feature space, pulling the centers of samples with the same identity closer, and pushing away the centers of samples with different identities while aggregating samples with the same modality and the same identity. Furthermore, we propose a Modality Bridge Similarity Optimization Strategy (MBSOS) to optimize the cross-modality similarity between the query and queried samples with the help of the similar bridge sample in the gallery. Extensive experiments demonstrate that compared to the original performance of existing cross-modality methods on MM-ReID, the addition of our CIDHL and MBSOS demonstrates a general improvement.
Abstract (translated)
当前的可见光-红外跨模态行人再识别研究仅集中在探索双模态互检范式上，我们提出了一种新的、更实用的混合模态检索范式。现有的可见光-红外行人再识别（VI-ReID）方法通过学习可见光和红外模态之间的对应关系，在双模态互检范式中取得了一些成果。然而，当这些方法应用于新的混合模态范式时，由于模态混淆问题导致性能显著下降。因此，本文提出了一个混合模态行人再识别（MM-ReID）任务，探讨了模态混合比例对性能的影响，并根据新的混合模态测试范式为现有数据集构建了混合模态测试集。为了在MM-ReID中解决模态混淆问题，我们提出了一种跨身份判别和谐损失（CIDHL），调整超球特征空间中的样本分布，拉近具有相同身份的样本中心的距离，并将不同身份的样本中心推开，同时聚集同一模态和相同身份的样本。此外，我们还提出了一个模态桥相似性优化策略（MBSOS），利用图像库中的类似桥梁样本帮助优化查询与被查询样本之间的跨模态相似度。广泛的实验表明，与现有跨模态方法在MM-ReID上的原始性能相比，加入我们的CIDHL和MBSOS显示出了一般性的改进。
URL
https://arxiv.org/abs/2412.04719
