Clothes-changing person re-identification (CC-ReID) aims to retrieve images
of the same person wearing different outfits. Mainstream researches focus on
designing advanced model structures and strategies to capture identity
information independent of clothing. However, the same-clothes discrimination
as the standard ReID learning objective in CC-ReID is persistently ignored in
previous researches. In this study, we dive into the relationship between
standard and clothes-changing~(CC) learning objectives, and bring the inner
conflicts between these two objectives to the fore. We try to magnify the
proportion of CC training pairs by supplementing high-fidelity clothes-varying
synthesis, produced by our proposed Clothes-Changing Diffusion model. By
incorporating the synthetic images into CC-ReID model training, we observe a
significant improvement under CC protocol. However, such improvement sacrifices
the performance under the standard protocol, caused by the inner conflict
between standard and CC. For conflict mitigation, we decouple these objectives
and re-formulate CC-ReID learning as a multi-objective optimization (MOO)
problem. By effectively regularizing the gradient curvature across multiple
objectives and introducing preference restrictions, our MOO solution surpasses
the single-task training paradigm. Our framework is model-agnostic, and
demonstrates superior performance under both CC and standard ReID protocols.

该研究采用多目标优化解决方案，通过衣物变化合成和训练，成功改善了衣物变换人员识别模型在衣物保持不变条件下的性能，同时在标准人员识别任务中也表现出了卓越的性能。

重新思考衣物更换人物识别：冲突、综合与优化

Rethinking Clothes Changing Person ReID: Conflicts, Synthesis, and  Optimization

Current clothes-changing person re-identification (re-id) approaches usually
perform retrieval based on clothes-irrelevant features, while neglecting the
potential of clothes-relevant features. However, we observe that relying solely
on clothes-irrelevant features for clothes-changing re-id is limited, since
they often lack adequate identity information and suffer from large intra-class
variations. On the contrary, clothes-relevant features can be used to discover
same-clothes intermediaries that possess informative identity clues. Based on
this observation, we propose a Feasibility-Aware Intermediary Matching (FAIM)
framework to additionally utilize clothes-relevant features for retrieval.
Firstly, an Intermediary Matching (IM) module is designed to perform an
intermediary-assisted matching process. This process involves using
clothes-relevant features to find informative intermediates, and then using
clothes-irrelevant features of these intermediates to complete the matching.
Secondly, in order to reduce the negative effect of low-quality intermediaries,
an Intermediary-Based Feasibility Weighting (IBFW) module is designed to
evaluate the feasibility of intermediary matching process by assessing the
quality of intermediaries. Extensive experiments demonstrate that our method
outperforms state-of-the-art methods on several widely-used clothes-changing
re-id benchmarks.

提出了一个基于衣物相关特征的可行性感知中介匹配（Feasibility-Aware Intermediary Matching，FAIM）框架，通过中介匹配过程中使用衣物相关特征找到信息丰富的中介并结合这些中介的衣物无关特征完成匹配，以此优化衣物变换的人物识别方法。实验结果表明，在多个广泛使用的衣物变换人物识别基准上，该方法优于现有方法。

具备可行性感知的衣着变换人体重识别

Clothes-Changing Person Re-Identification with Feasibility-Aware  Intermediary Matching

The key to address clothes-changing person re-identification (re-id) is to
extract clothes-irrelevant features, e.g., face, hairstyle, body shape, and
gait. Most current works mainly focus on modeling body shape from
multi-modality information (e.g., silhouettes and sketches), but do not make
full use of the clothes-irrelevant information in the original RGB images. In
this paper, we propose a Clothes-based Adversarial Loss (CAL) to mine
clothes-irrelevant features from the original RGB images by penalizing the
predictive power of re-id model w.r.t. clothes. Extensive experiments
demonstrate that using RGB images only, CAL outperforms all state-of-the-art
methods on widely-used clothes-changing person re-id benchmarks. Besides,
compared with images, videos contain richer appearance and additional temporal
information, which can be used to model proper spatiotemporal patterns to
assist clothes-changing re-id. Since there is no publicly available
clothes-changing video re-id dataset, we contribute a new dataset named CCVID
and show that there exists much room for improvement in modeling spatiotemporal
information. The code and new dataset are available at:
this https URL

该研究提出了一种基于衣服的对抗损失 (CAL) 方法，从原始 RGB 图像中挖掘与衣服无关的特征来提高衣服变化人员重新识别的准确度。同时，也贡献了一个名为 CCVID 的数据集，为对建模时空信息提供实验基础。