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

Cloth-Changing Person Re-Identification (CC-ReID) aims to accurately identify
the target person in more realistic surveillance scenarios, where pedestrians
usually change their clothing. Despite great progress, limited cloth-changing
training samples in existing CC-ReID datasets still prevent the model from
adequately learning cloth-irrelevant features. In addition, due to the absence
of explicit supervision to keep the model constantly focused on
cloth-irrelevant areas, existing methods are still hampered by the disruption
of clothing variations. To solve the above issues, we propose an Identity-aware
Dual-constraint Network (IDNet) for the CC-ReID task. Specifically, to help the
model extract cloth-irrelevant clues, we propose a Clothes Diversity
Augmentation (CDA), which generates more realistic cloth-changing samples by
enriching the clothing color while preserving the texture. In addition, a
Multi-scale Constraint Block (MCB) is designed, which extracts fine-grained
identity-related features and effectively transfers cloth-irrelevant knowledge.
Moreover, a Counterfactual-guided Attention Module (CAM) is presented, which
learns cloth-irrelevant features from channel and space dimensions and utilizes
the counterfactual intervention for supervising the attention map to highlight
identity-related regions. Finally, a Semantic Alignment Constraint (SAC) is
designed to facilitate high-level semantic feature interaction. Comprehensive
experiments on four CC-ReID datasets indicate that our method outperforms prior
state-of-the-art approaches.

为了解决现有 CC-ReID 数据集中缺乏足够训练样本的问题，本研究提出了一种 Identity-aware Dual-constraint Network (IDNet)。通过引入 Clothes Diversity Augmentation (CDA)、Multi-scale Constraint Block (MCB)、Counterfactual-guided Attention Module (CAM) 和 Semantic Alignment Constraint (SAC) 等模块，该方法能够在更真实的监控场景中准确识别目标人员，并在实验中表现出超越现有方法的性能。

面向衣物更换的人物重新识别的身份感知双约束网络

Identity-aware Dual-constraint Network for Cloth-Changing Person  Re-identification

Cloth-changing person re-identification (CC-ReID) aims to match persons who
change clothes over long periods. The key challenge in CC-ReID is to extract
clothing-independent features, such as face, hairstyle, body shape, and gait.
Current research mainly focuses on modeling body shape using multi-modal
biological features (such as silhouettes and sketches). However, it does not
fully leverage the personal description information hidden in the original RGB
image. Considering that there are certain attribute descriptions which remain
unchanged after the changing of cloth, we propose a Masked Attribute
Description Embedding (MADE) method that unifies personal visual appearance and
attribute description for CC-ReID. Specifically, handling variable
clothing-sensitive information, such as color and type, is challenging for
effective modeling. To address this, we mask the clothing and color information
in the personal attribute description extracted through an attribute detection
model. The masked attribute description is then connected and embedded into
Transformer blocks at various levels, fusing it with the low-level to
high-level features of the image. This approach compels the model to discard
clothing information. Experiments are conducted on several CC-ReID benchmarks,
including PRCC, LTCC, Celeb-reID-light, and LaST. Results demonstrate that MADE
effectively utilizes attribute description, enhancing cloth-changing person
re-identification performance, and compares favorably with state-of-the-art
methods. The code is available at this https URL

通过使用属性描述信息以及遮盖和嵌入技术，提出了一种名称为 MADE 的方法，能够增强衣物变化人员重新识别的性能，并与最先进的方法相比取得了良好的效果。

面具化属性描述嵌入用于服装更换人员再识别

Masked Attribute Description Embedding for Cloth-Changing Person  Re-identification

Cloth-Changing person re-identification (CC-ReID) aims at matching the same
person across different locations over a long-duration, e.g., over days, and
therefore inevitably meets challenge of changing clothing. In this paper, we
focus on handling well the CC-ReID problem under a more challenging setting,
i.e., just from a single image, which enables high-efficiency and latency-free
pedestrian identify for real-time surveillance applications. Specifically, we
introduce Gait recognition as an auxiliary task to drive the Image ReID model
to learn cloth-agnostic representations by leveraging personal unique and
cloth-independent gait information, we name this framework as GI-ReID. GI-ReID
adopts a two-stream architecture that consists of a image ReID-Stream and an
auxiliary gait recognition stream (Gait-Stream). The Gait-Stream, that is
discarded in the inference for high computational efficiency, acts as a
regulator to encourage the ReID-Stream to capture cloth-invariant biometric
motion features during the training. To get temporal continuous motion cues
from a single image, we design a Gait Sequence Prediction (GSP) module for
Gait-Stream to enrich gait information. Finally, a high-level semantics
consistency over two streams is enforced for effective knowledge
regularization. Experiments on multiple image-based Cloth-Changing ReID
benchmarks, e.g., LTCC, PRCC, Real28, and VC-Clothes, demonstrate that GI-ReID
performs favorably against the state-of-the-arts. Codes are available at
this https URL

本文提出一种名为 GI-ReID 的框架，利用人体独特的不依赖于衣服的步态信息作为辅助任务，从单张图像识别行人并实现实时监控，并在衣服变化的 ReID 标准测试集上得到了显著的性能提升。