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Camera-aware unsupervised person re-identification method guided by pseudo-label refinement
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摘要
无监督行人重识别因其广泛的实际应用前景而受到越来越多的关注。大多数基于聚类的对比学习方法将每个集群视为一个伪身份类，忽略了由相机风格差异造成的类内差异。一些方法引入了相机感知对比学习，根据相机视角将单一集群划分为多个子集群，但它们容易受到噪声伪标签的误导。为解决这一问题，本文首先基于实例在特征空间中的相似性，采用最近邻的预测标签和原始聚类结果的加权组合细化伪标签。然后，采用细化伪标签动态地关联实例可能属于的类别中心，同时剔除可能存在的假阴性样本。这一方法改进了相机感知对比学习中正负样本的选择机制，有效地减轻了噪声伪标签对对比学习任务的误导。在Market-1501、MSMT17、Personx数据集上mAP/Rank-1分别达到了85.2%/94.4%、44.3%/74.1%、88.7%/95.9%。
Abstract
Unsupervised person re-identification has attracted more and more attention due to its extensive practical application prospects. Most clustering-based contrastive learning methods treat each cluster as a pseudo-identity class, overlooking intra-class variances caused by differences in camera styles. While some methods have introduced camera-aware contrastive learning by partitioning a single cluster into multiple sub-clusters based on camera views, they are susceptible to misguidance from noisy pseudo-labels. To address this issue, we first refine pseudo-labels by leveraging the similarity between instances in the feature space, using a weighted combination of the nearest neighboring predicted labels and the original clustering results. Subsequently, it dynamically associates instances with possible category centers based on refined pseudo-labels while eliminating potential false negative samples. This method enhances the selection mechanism for positive and negative samples in camera-aware contrastive learning, effectively mitigating the influence of noisy pseudo-labels on the contrastive learning task. On Market-1501, MSMT17 and Personx datasets, mAP/Rank-1 reached 85.2%/94.4%, 44.3%/74.1% and 88.7%/95.9%.
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Overview
Overview: Unsupervised person re-identification has received increasing attention due to its wide practical application prospects. Most clustering-based contrastive learning methods treat each cluster as a pseudo-identity class, focusing on improving inter-class differences while ignoring intra-class differences caused by factors such as perspective, lighting, and background between different cameras. This makes it difficult for clustering algorithms to accurately cluster samples with the same identity into the same cluster, inevitably leading to noisy pseudo-labels. Some methods have introduced camera-aware contrastive learning, which divide a single cluster into multiple sub-clusters based on the camera's perspective, and calculate the intra-camera and inter-camera contrastive loss separately. However, the noise in pseudo-labels may interfere with the selection of positive and negative samples in camera-aware contrastive learning, thereby misleading the model's learning process. To address this issue, this paper proposes a camera-aware unsupervised person re-identification method guided by refined pseudo-labels. By calculating the similarity between training instances in feature space, a neighborhood set is determined for each instance. Subsequently, the model refines one-hot pseudo-labels by combining the predicted labels for samples within the neighborhood with the original clustering results using weighted aggregation. The core idea behind this approach is to encourage the model to not only bring samples closer to their respective cluster centers but also establish associations with other nearby samples that may contain identity information. This strategy effectively enhances the model's robustness against noisy labels while reducing the risk of over-fitting. Building upon this, this paper further proposes camera-aware contrastive learning guided by refined pseudo-labels. By leveraging the probability distribution of each class in the refined pseudo-labels for instances, the model dynamically associates instances with potential class centers, no longer relying on a single class center as the positive sample. Additionally, potential false positive and false negative samples are filtered out. This method enhances the selection mechanism of positive and negative samples in camera-aware contrastive learning, effectively mitigating the influence of noisy pseudo-labels on the contrastive learning task. The method proposed in this article was validated on three large-scale public datasets, and the results showed that this method has significantly improved compared to the baseline method and is superior to current advanced methods in the same field. This method achieved mAP/Rank-1 of 85.2%/94.4%, 44.3%/74.1%, and 88.7%/95.9% on the Market-1501, MSMT17, and Personx datasets, respectively, demonstrating superiority. Specifically, on the Market-1501, MSMT17, and Personx datasets, this paper’s method achieves mAP/Rank-1 scores of 85.2%/94.4%, 44.3%/74.1%, and 88.7%/95.9%, respectively, showcasing its superiority.
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图 3 细化伪标签引导相机感知示意图。(a) 原始相机内对比; (b) 校正后相机内对比; (c) 原始相机间对比; (d) 校正后相机间对比
Figure 3. Schematic diagram of camera-aware guided by refined pseudo-labels. (a) Original intra-camera contrast; (b) Corrected intra-camera contrast; (c) Original inter-camera contrast; (d) Corrected inter-camera contrast
图 6 各超参数在Market-1501数据集上对本文模型的影响。(a) $\alpha $; (b) $k $; (c) $\tau_{\rm{intra}} $; (d) $\tau_{\rm{inter}} $
Figure 6. The impact of each hyperparameter to our model on Market-1501. (a) $\alpha $; (b) $k $; (c) $\tau_{\rm{intra}} $; (d) $\tau_{\rm{inter}} $
表 1 本文方法与最新方法的比较
Table 1. The comparison between the our method and the latest methods
Methods Market-1501 MSMT17 Personx mAP/% Rank-1/% mAP/% Rank-1/% mAP/% Rank-1/% Unsupervised domain adaptation (UDA) ECN[2] CVPR’19 43.0 75.1 10.2 30.2 - - SPCL[3] NeurIPS’20 77.5 89.7 26.8 53.7 78.5 91.1 MEB-Net[13] ECCV’20 76.0 89.9 - - - - MMT[12] CVPR’21 71.2 87.7 23.5 50.0 78.9 90.6 GLT[31] CVPR’21 79.5 92.2 27.7 59.5 - - MCL[32] JBUAA’22 80.6 93.2 28.5 58.5 - - CACHE[33] TCSVT’22 83.1 93.4 31.3 58.0 - - CIFL[20] TMM’22 83.3 93.9 39.0 70.5 - - MCRN[16] AAAI’22 83.8 93.8 35.7 67.5 - - IICM[34] JCRD’23 74.9 89.0 27.2 52.3 - - NPSS[21] TIFS’23 84.6 94.1 38.9 69.4 - - CaCL[11] ICCV’23 84.7 93.8 40.3 70.0 - - Unsupervised learning (USL) SPCL[3] NeurIPS’20 73.1 88.1 19.1 42.3 72.3 88.1 MetaCam[7] CVPR’21 61.7 83.9 15.5 35.2 - - IICS[8] CVPR’21 72.9 89.5 26.9 56.4 - - RLCC[14] CVPR’21 77.7 90.8 27.9 56.5 - - CAP[9] AAAI’21 79.2 91.4 36.9 67.4 - - ICE[17] ICCV’21 82.3 93.8 38.9 70.2 - - CACHE[33] TCSVT’22 81.0 92.0 31.8 58.2 - - CIFL[20] TMM’22 82.4 93.9 38.8 70.1 - - GRACL[35] TCSVT’22 83.7 93.2 34.6 64.0 87.9 95.3 PPLR[15] CVPR’22 84.4 94.3 42.2 73.3 - - CA-UReID[10] ICME’22 84.5 94.1 - - - NPSS[21] TIFS’23 82.3 94.0 36.7 68.8 - - LRMGFS[36] JEMI’23 83.3 93.3 27.4 58.4 - - PLRIS[22] ICIP’23 83.2 93.1 43.3 71.5 - - AdaMG[37] TCSVT’23 84.6 93.9 38.0 66.3 87.6 95.0 LP[18] TIP’23 85.8 94.5 39.5 67.9 - - DCCT[19] TCSVT’’23 86.3 94.4 41.8 68.7 87.6 95.0 CC[4] CoRR’21 82.1 92.3 27.6 56.0 84.7 94.4 Ours - 85.2 94.4 44.3 74.1 88.7 95.9 
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表 2 Market-1501数据集上消融实验结果
Table 2. Results of ablation studies on Market-1501
${{\widetilde {\rm{L}} }_{ {\text{ce} } } }$ ${ {\widetilde {\rm{L}} }_{ {\text{intra} } } }$ ${ {\widetilde {\rm{L}} }_{ {\text{inter} } } }$ ${\rm{Market} }\text{-}1501$ ${\rm{Personx}} $ mAP/% Rank-1/% Rank-5/% Rank-10/% mAP/% Rank-1/% Rank-5/% Rank-10/% M1(CC[4]) - - - 82.1 92.3 96.7 97.9 84.7 94.4 98.3 99.3 M2 - √ - 82.9 93.2 97.3 98.2 87.9 95.7 98.8 99.5 M3 - - √ 83.9 93.6 97.5 98.3 87.3 95.9 98.8 99.4 M4 - √ √ 84.1 93.6 97.6 98.5 88.5 95.8 98.9 99.6 M5 √ - - 84.7 94.2 97.9 98.7 87.5 95.5 98.9 99.5 M6(Ours) √ √ √ 85.2 94.4 98.1 98.6 88.7 95.9 99.2 99.7
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