Title: Spatiotemporal Consensus with Scene Prior for Unsupervised Domain Adaptive Person Search
Venue: NeurIPS 2025
Accepted: yes
Keywords: Person Search
Abstract: Person Search aims to locate query persons in gallery scene images, but faces severe performance degradation under domain shifts. Unsupervised domain adaptation transfers knowledge from the labeled source domain to the unlabeled target domain and iteratively rectifies the pseudo-labels. However, the pseudo-labels are inevitably contaminated by the source-biased model, which misleads the training process. This, in turn, reduces the quality of the pseudo-labels themselves and ultimately affects the search performance. In this paper, we propose a Spatiotemporal Consensus with Scene Prior (STCSP) framework that effectively eliminates the interference of noise on pseudo-labels, establishes positive feedback, and thus gradually bridging the domain gap. Firstly, STCSP uses a Spatiotemporal Consensus pipeline to suppress the noise from being mixed into the pseudo-labels. Secondly, leveraging the scene prior, STCSP employs our designed Iterative Bilateral Extremum Matching method to prevent the occurrence of some incorrect pseudo-labels. Thirdly, we propose a Scene Prior Contrastive Learning module, which encourages the model to directly acquire the scene prior knowledge from the target domain, thereby mitigating the generation of noise. By suppressing noise contamination, avoiding noise occurrence and mitigating noise generation, our framework achieves state-of-the-art performance on two benchmark datasets, PRW with 50.2% mAP and CUHK-SYSU with 87.0% mAP.

Spatiotemporal Consensus with Scene Prior for Unsupervised Domain Adaptive Person Search

Abstract

Person Search aims to locate query persons in gallery scene images, but faces severe performance degradation under domain shifts. Unsupervised domain adaptation transfers knowledge from the labeled source domain to the unlabeled target domain and iteratively rectifies the pseudo-labels. However, the pseudo-labels are inevitably contaminated by the source-biased model, which misleads the training process. This, in turn, reduces the quality of the pseudo-labels themselves and ultimately affects the search performance. In this paper, we propose a Spatiotemporal Consensus with Scene Prior (STCSP) framework that effectively eliminates the interference of noise on pseudo-labels, establishes positive feedback, and thus gradually bridging the domain gap. Firstly, STCSP uses a Spatiotemporal Consensus pipeline to suppress the noise from being mixed into the pseudo-labels. Secondly, leveraging the scene prior, STCSP employs our designed Iterative Bilateral Extremum Matching method to prevent the occurrence of some incorrect pseudo-labels. Thirdly, we propose a Scene Prior Contrastive Learning module, which encourages the model to directly acquire the scene prior knowledge from the target domain, thereby mitigating the generation of noise. By suppressing noise contamination, avoiding noise occurrence and mitigating noise generation, our framework achieves state-of-the-art performance on two benchmark datasets, PRW with 50.2% mAP and CUHK-SYSU with 87.0% mAP.

1 Introduction

Person search aims to localize and identify a query person from a gallery of scene images. It can be taken as a joint task of person detection and re-identification (re-id) in an end-to-end manner, where supervised learning has made significant advancements. However, notable performance degradation is observed in these methods when deployed to new application scenarios, primarily attributed to domain gaps induced by factors such as camera configuration. Additionally, the process of annotating an adequate amount of training data for a particular domain is both arduous and costly. Consequently, unsupervised domain adaptation (UDA) holds considerable promise in practical real-world scenarios for person search.

UDA in person search aims to achieve model generalization from labeled source data to unlabeled target deployment scenarios. DAPS [11], a pioneering work in applying UDA to person search, employs implicit domain alignment in conjunction with a pseudo-labeling method to effectively bridge the gap between the source and target domains. Building upon DAPS, Almansoori et al. proposed DDAM [1], which generates a hybrid domain and learns within it to minimize the distance between the two domains. Although these methods achieve satisfactory performance, they failed to

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effectively eliminate the interference of noise stemming from the source-biased models on pseudolabels. As illustrated in Fig. 1b, the inaccurate bounding boxes (BBoxes) inevitably lead to detection failures, which can induce significant degradation in person search performance. Additionally, as shown in Fig. 1c, the incorrect labels will directly cause confusion in identifying the person identities. Furthermore, the noise accumulates with the increase of epochs, forming a negative feedback mechanism that will cause performance degradation of the model during the later training phase. Therefore, eliminating the interference of the noise on pseudo-labels is a major challenge. In addition, previous studies failed to realize that a scene can provide relationships for its inner persons. Specifically, the identities of persons within a scene image are inherently distinct, and this is referred to as scene prior. The omission of considering and exploiting this scene prior results in a conspicuous error as illustrated in Fig. 1d. Consequently, the utilization of the scene prior is another crucial issue.

To address these issues, we propose a novel Spatiotemporal Consensus with Scene Prior (STCSP) framework that eliminates the impact of the noise through suppressing noise contamination, preventing some erroneous pseudo-labels and mitigating noise generation. Our framework progressively bridges the domain gap via three core innovations. First, we devise a Spatiotemporal Consensus (STC) pipeline to suppress noise propagation in pseudo-labels. STC maintains a memory bank of the previous detection and re-id information. To filter out temporal jitters, STC leverages the temporal consensus between the information in the memory bank and the current state. In addition, for the detection and re-id subtasks respectively, STC exploits the spatial consensus in the scene space of the image and the latent space of clustering to eliminate spatial outliers. Second, leveraging the intrinsic scene prior, STCSP conducts bipartite matching for the instances in any two of all the images, thus averting the error depicted in Fig. 1d. However, deriving the optimal solutions for bipartite matching across thousands of images is computationally prohibitive. To tackle this challenge, we propose an Iterative Bilateral Extremum Matching (IBEM) method, leveraging GPU acceleration, to seek the approximate solutions. Third, we propose a Scene Prior Contrastive Learning (SPCL) module that encourages the model to learn discriminative person features within the target domain. Specifically, besides proxy learning, SPCL steers the model to learn identity heterogeneity within the same scene, enabling the model to directly absorb knowledge from the target domain. The existence of this knowledge effectively reduces the noise output of the model.

Our major contributions are summarized below:

A Spatiotemporal Consensus pipeline is proposed to suppress the noise origination from the domain gap, thereby generating reliable pseudo-labels.

An Iterative Bilateral Extremum Matching method is designed. Within a few seconds, it can match the instances in any two of all thousands of images.

A Scene Prior Contrastive Learning module is proposed, which encourages the model to directly acquire knowledge from the target domain.

With the spatiotemporal consensus and the scene prior, STCSP achieves state-of-the-art (SOTA) performance on two benchmark datasets. Specifically, it attains a mean average precision of 50.2% on the PRW dataset and 87.0% on the CUHK-SYSU dataset.

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2 Method

2.1 Framework Overview

Our UDA person search model is an end-to-end architecture based on SeqNet [12]. Differently, we replaced the backbone network and person feature embedder with ConvNeXt-B [16] and our designed attention embedder, respectively. Following the approach of DAPS [11], we introduce gradient reversal layers (GRLs) and domain discriminators (DDs) for three level features. We adhere to the principle of 'sample quality first' and use our STC pipeline to obtain reliable pseudo-labels. The SPCL and task cooperation (TC) modules are employed to steer our model to learn discriminative features.

Prior to the training of each epoch, the STC pipeline preprocesses the unlabeled target domain. As in the first row of Fig. 2, the temporal consensus updates the memory bank with model-inferred BBox scores, BBoxes, and embeddings. The spatial consensus then screens out pseudo-BBoxes from the memory bank. Subsequently, their embeddings are calculated by Re-Ranking [30] to form a cross-distance matrix. This matrix undergoes bipartite matching via the IBEM method, and the result is used for matrix sparsity regularization. The regularized matrix generates spatial consensus clusters, which combine with those from the previous epoch to form spatiotemporal ones. The center of each cluster serves as a proxy sent to the SPCL.

During the training phase, the model is fed source and target domain mini-batches in an alternating sequence, with parameters being updated every two mini-batches. As shown in the second row of Fig. 2, the first four ConvNeXt-B [16] stages are responsible for extracting scene features. The feature map is then fed into the Faster-RCNN-based [20] detection module for BBox generation, where RoI-Align pools the instance feature map for each BBox and the Attention Embedder encodes it into an embedding. Finally, the embedding is sent to the TC and SPCL modules for loss calculation.

2.2 Spatiotemporal Consensus for Detection

Temporal Consensus for Detection. For the estimated BBoxes H inferred by the model and the anchor BBoxes G stored in the memory bank, we use the intersection-over-union (IoU) scores as the basis for the matching between them. The unmatched BBoxes and their accompanying BBox scores and instance embeddings in the memory bank will be removed, and the remaining contents will be

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updated with momentum η . The matching and updating operations are expressed as follows:

where ˜ G is a set of updated anchor BBoxes, ρ is a box IoU function and θ is a threshold for the IoU score. In this equation, the anchor BBox g and the estimated BBox h are mutually IoU-optimal, and the score also meets the threshold. Matching the anchor with the maximum IoU for each estimate is to avoid updating multiple anchors for one estimate. Based on the assumption that the pseudo-labels are true, we consider each anchor to be true. Therefore, selecting the estimate with the maximum IoU is equivalent to maximizing the likelihood probability of generating that anchor.

In addition, the estimated BBoxes in H that are independent of all anchor BBoxes are used to generate new anchors. Whose definition is as follows:

The boxes in ̂ H with a high IoU score between them will be merged to ensure the independence among the new anchors. First, a variant of non-maximum suppression (NMS) operation with threshold θ is applied to divide ̂ H into multiple subsets, as follows:

Specifically, a box retained by the NMS and the boxes it suppresses are grouped together. Finally, the mean values of each subset from the new anchors ̂ G . Notably, the score and embedding corresponding to each BBox will undergo the same operations during this process. Each update of the memory bank is a deep integration of historical and current knowledge. Therefore, the result G ← ˜ G ∪ ̂ G is a temporal consensus.

Spatial Consensus for Detection. The comprehensive analysis of Eqs. (1) to (3) reveals that the maximum value of the IoU scores among the anchor BBoxes in the memory bank is approximately θ . This indicates that there may be multiple BBoxes stacked on one person instance, as shown in the right of Fig. 1b. Therefore, we apply a NMS with threshold φ to screen out reliable BBoxes from the memory bank, thereby establishing spatial consensus on each image.

2.3 Measurement with Scene Prior

Following previous works, we adopt Re-Ranking [30] method to compute pairwise distances between instance embeddings. To optimize computational performance, we have developed a GPU-accelerated implementation using PyTorch. However, different from previous works, we find that scenes prior can sparsely regularize the distance matrix, thus avoiding the error as shown in Fig. 1d. Specifically, our approach constructs inter-scene cost matrices from the instance distances, performing minimum bipartite matching to set unmapped pair distances to infinity, thereby enforcing intra-scene identity uniqueness. However, the O ( n 3 ) omplexity of exact solvers like Hungarian algorithm [10] becomes prohibitive at scale ( ≈ 2 hours / 10 k images).

Iterative Bilateral Extremum Matching. To relieve this dilemma, we propose an IBEM method to calculate the approximate solution. Specifically, as shown in algorithm 1, the IBEM iteratively identifies entries that are extrema ( e.g. , minima or maxima) in both their row and column, then removes the matched entries' rows and columns for further iterations until the conditions are met.

Algorithm 1 Iterative Bilateral Extremum Matching

Require: Cost matrix C = { c , · · · } and its row and column indices I and J .

Ensure: Matching pairs: M = M ∪ M ∪ · · · ∪ M

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Although some rows and columns may remain unmatched during each iteration of bilateral extremum matching (BEM), we rigorously prove by contradiction that a scenario with complete unmatching of all rows and columns is theoretically impossible. Consequently, full instance matching is guaranteed via the iterative process. To balance computational efficiency and convergence speed, we introduce two hyperparameters, an unmatched proportion threshold ϑ and a maximum number of iterations δ as limitations. Fig. 3 shows an example of this method. Notably, by leveraging GPU acceleration, IBEM completes cross-image matching across thousands of instances within seconds.

2.4 Spatiotemporal Consensus for Re-ID

Spatial Consensus for Re-ID. Conceptually, a discriminative cluster requires sufficient separation from other instances. However, the uneven density of the latent space renders a fixed-distance threshold inadequate for the specificity evaluation. To address this challenge, we propose a spatial clustering consensus strategy, which identifies reliable clusters through dual-granularity pattern analysis. Specifically, we implement a dual-density DBSCAN strategy: A conservative density with radius ϵ and min sample size κ captures coarse-grained clusters, while an aggressive density with radius α · ϵ and min sample size α · κ detects fine-grained clusters. Then, through the cluster consensus method, our strategy synthesizes spatial consensus clusters, as shown on the first row of Fig. 2. This cross-validation mechanism effectively distinguishes reliable clusters, ensuring robustness against the latent space with uneven density.

Temporal Consensus for Re-ID. As the instances iteratively update their latent representations, the temporally stable clusters maintain structural cohesion despite positional shifts. Therefore, to screen out the clusters with temporal robustness, we propose a temporal clustering consensus strategy, which integrates the spatial consensus results across previous and current epochs. This cross-epoch fusion process identifies the persistent clusters by the cluster consensus method, thereby forming temporal consensus results. Specifically, as shown on the first row of Fig. 2, the temporal consensus window is constrained to two adjacent epochs, enforcing gradual cluster evolution while preserving temporal consensus across training iterations.

Cluster Consensus. For a certain element ξ in a set Ξ = { ξ 1 , ξ 2 , · · · , ξ n } containing n instances, there are two different strategies, Φ and Ψ , to obtain its cluster labels Φ( ξ, Ξ) and Ψ( ξ, Ξ) respectively. So, in strategy Φ , the cluster which ξ belongs to is as follows:

Based on F , an intersection-over-union score of the clusters where ξ is located in Φ and Ψ can be calculated as follows:

where F( ξ, Ξ , Φ) and F( ξ, Ξ , Ψ) represent the clusters where ξ is placed in Φ and Φ , respectively. Then, the maximum IoU score among all instances in F( ξ, Ξ , Φ) is as follows:

Finally, a cluster consensus label for ξ is obtained through the following conditional mapping:

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where f ( ξ, Ξ , Φ , Ψ) = g( ξ, Ξ , Φ , Ψ) means that ξ lies in the intersection of F( ξ, Ξ , Φ) and F( ξ, Ξ , Ψ) , which has the maximum IoU score within F( ξ, Ξ , Φ) . Moreover, we enforce a 0 . 5 IoU threshold to validate high confidence in Φ and Ψ consensus. Besides, an instance with cluster label of -1 denotes an outlier.

2.5 Scene Prior Contrastive Learning

Existing works have achieved satisfactory results through contrastive learning using person feature proxies. However, they failed to realize and utilize scene prior to guide the model learning. Therefore, we design a triplet loss function to encourage the model to learn this prior knowledge. Specifically, we introduce a person proxy table (PT) V = { v 1 , v 2 , · · · } for the target domain. Before each training epoch, the PT will be re-formed by taking the mean value of embeddings in each cluster. And, during the training, it is updated online with a momentum λ . Given a set I of the instance feature embeddings in a scene image, the margin m of an embedding x in I is calculated as follows:

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where ⟨· , ·⟩ denotes the inner product between two embeddings and L( · ) represents taking the label corresponding to the embedding. v L( x ) is the proxy in V corresponding to the x and A is all the embeddings in I that have different labels from x . Besides, we use a hyper-parameter M to evaluate the margin. And the triplet loss objective is to increase the margin m to M :

In addition, we also employ a cross-entropy loss in this module. It calculates the probability that the label of x is L( x ) , and uses cross-entropy to supervised the probability.

where τ = 1 / 30 is hyper-parameter which adjusts the softness of the probability distribution. U and Q are respectively the lookup table (LUT) and circular queue (CQ) proposed by OIM, which are updated by the embeddings generated from the source domain. Finally, the scene prior contrastive learning loss is obtained by adding two terms together:

2.6 Task Cooperation

BNR [9] loss utilizes the foreground and background labels from the detection head to supervise the embeddings generated by the re-id head. It can encourage the backbone to enhance the foreground features and suppress the background features, thereby improving the model's detection and reid performance simultaneously. We improved the equation for calculating the probability that an embedding x belongs to the foreground as follows:

where σ ( · ) is a sigmoid function, BN( · ) is an batch normalization [8] layer, and ∥ ξ ∥ 2 is the L 2 -norm value of the feature embedding x . We employ the square L 2 -norm as the regularization term to impose independent constraints on each dimension of x and prevent outliers in some dimensions from influencing others through cross-term interactions. And dividing by 2 is to simplify the derivative operation. Finally, Focal Loss [14] is calculated for p , which focus training on the hard negatives:

where y is a label (either 0 or 1 ) indicating whether its corresponding embedding is marked as a background or person.

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2.7 Attention Embedder

Inspired by the attention pool module in CLIP [19], we propose a novel attention embedder as shown in Fig. 4. Distinct from the attention pool module, our design introduces two critical enhancements to the multi-head attention output: Batch Normalization [8] (BN) and Dropout [21]. BN standardizes the distribution of each embedding dimension by centering it near zero with unit variance, mitigating its congregation in positive or negative ranges. This normalization encourages a discriminative embedding and enhance its expressivity in the latent space. Subsequently, Dropout injects stochasticity during training, preventing over-reliance on specific embedding dimensions. Through the synergism of these components, a discriminative and robust embedding is obtained.

3 Experiments

3.1 Experimental Setup and Implementation Details

CUHK-SYSU [25] is a large-scale person search dataset that contains 18,184 images collected from handheld cameras, movies, and TV shows, resulting in significant scene diversity. It encompasses 8,432 unique person identities and 96,143 annotated bounding boxes. The dataset is split into a training set with 5,532 identities and 11,206 images, and a test set with 2,900 queries and 6,978 gallery images. For each query, the dataset defines a gallery size ranging from 50 to 4,000, with a default gallery size of 100 images.

PRW [29] is composed of video frames captured by six fixed cameras on a university campus. It contains 11,816 scene images with 932 distinct person identities and 43,110 annotated bounding boxes. The training set consists of 483 identities with 5,704 images, and the test set contains 2,057 queries and 6,112 scene images. For each query, the dataset uses all images in the test set except for the query as the gallery.

Evaluation Metric. Following the established setting in previous work, the mean Average Precision (mAP) and top-1 accuracy (top-1) are employed to evaluate the performance for person search.

Implementation Details. We conduct all experiments on a NVIDIA A800 GPU and implement our model with PyTorch [18]. ConvNeXt [16] pre-trained on ImageNet [5] is adopted as the backbone network. During training, we set the batch size to 4, and use Automatic Mixed Precision (AMP). Adam is used to optimize our model for 20 epochs with an initial learning rate of 0.0001 and a weight decay of 0.01, which is warmed up during the first epoch and reduced by a factor of 0.1 at epochs 8 and 14. When PRW is used as the target domain, our model undergoes pre-training on the source domain CUHK-SYSU for 5 epochs before commencing joint training. Conversely, it pre-train on PRW for 3 epoch. The thresholds θ and φ in Sec. 2.2 are set to 0.7 and 0.4, respectively. For algorithm 1, the limitations δ and ϑ are set to 2 and 0.2, respectively. The hyper-parameter ϵ , κ and α in Sec. 2.4 is set to 0.5, 4 and 0.8, respectively. For Eq. (9), we initialize the hyper-parameter M as 0.35. For the attention embedder, the probability of the Dropout is set to 0.1.

3.2 Comparison to the State-of-the-Arts

Comparison on PRW. The 3rd column of Tab. 1 shows the performance of all methods on the PRW test set. As shown in the table, our framework outperforming all the other UDA and weaklyunsupervised methods, whether based on ConvNeXt or ResNet. Especially for the current bestperforming DDAM [1], STCSP outperforms it by 13.5% and 6.1% in mAP and top-1 accuracy, respectively. And when both employ ResNet-50 as the backbone, our method still outperform it by 5.7% and 0.9%. Notably, our method outperforms most fully supervised methods, despite they using ground truth labels to train models.





 



























(a) Comparison of mAP.





 

























(b) Comparison of top-1 accuracy.

Comparison on CUHK-SYSU. The performance of our method and the other methods on the CUHK-SYSU test set is shown in the 4th column of Tab. 1. STCSP based on ConvNeXt or ResNet outperforms all the other UDA methods. It achieves the best scores of 87.0% in mAP and 87.9% in top1 accuracy, surpassing DDAM [1] by 7.5% and 6.6% in mAP and top-1 accuracy, respectively. The CUHK-SYSU training set boasts greater diversity and a larger quantity of scene images compared to the PRW training set. As a result, UDA from PRW to CUHK-SYSU presents a formidable challenge. Despite this, our method still outperforms several weakly-supervised and fully-supervised methods on the CUHK-SYSU test set.

We further perform a comparison between our STCSP and other methods on the CUHK-SYSU test set with gallery sizes ranging from 50 to 4,000. Fig. 5 shows the performance curve of all the methods in terms of mAP and top-1 as the gallery size increases. Since it is a challenge for all compared methods to consider more distracting persons in the gallery set, the performance of them is reduced as the gallery size increases. However, our method consistently outperforms all the other methods in

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mAP and top-1, whether based on ConvNeXt or ResNet. This indicates that our method has excellent generalization ability.

Qualitative Results. Some example person search results are illustrated in Fig. 6. The 5th column of the figure shows the result of using a low-resolution image as the query. When multiple highly similar characters appear in a scene, the search results are illustrated in the 1st and 4th columns. The 2nd column shows the result of the person's body being obstructed. The 3rd and 6th columns show the result when the scene and viewpoint change. It can be observed that our successfully handles low-resolution, similar-character, occlusion, cross-scene and viewpoint variation, while other state-of-the-art methods such as DAPS fail in these scenarios. This demonstrates the robustness of our method.

3.3 Ablation Study

Spatiotemporal Consensus for Detection. To verify the effectiveness of our STC strategy for detection, we evaluate separately the performance of our framework in two scenarios: detection without spatial consensus and without temporal consensus, and report the results in Tab. 2. We find that STCSP exhibits significant performance degradation when lacking the spatial or temporal consensus for detection. This results indicate that the STC strategy for detection significantly elevates the quality of pseudo-BBoxes.

Spatiotemporal Consensus for Re-ID. Tab. 2 shows the performance of our framework being employed with different strategies on the two benchmarks. First. to validate the effectiveness of the spatial consensus, we replace it with a DBSCAN method. The results reveal that our strategy of employing a loose and compact clustering space to form spatial consensus can generate reliable clusters. Furthermore, The results after removal of the temporal consensus are also reported in Tab. 2. Through comparison, we can see that the temporal consensus improve the performance of our model

on both of PRW and CUHK-SYSU. We attribute these improvements to the stable clusters formed by the temporal consensus.

Scene Prior. We evaluated the influence exerted by the lack of scene priors within the related components on the performance manifestations of our framework, and report the results in Tab. 2. As presented in the 5th row of the table, when the IBEM is absent from our framework, entailing the potential occurrence of the error depicted in Fig. 1d, the performance of our framework experiences a substantial decline across both datasets. Notably, this decline is more pronounced on the PRW dataset. Similarly, as indicated in the 6th row of the table, a comparable scenario transpires when the triplet loss within the SPCL is lacking. The significant performance degradation observed on PRW can be attributed to the existence of persons with a high similarity within some scene images in this dataset. This indicates that scene prior effectively steers the model to learn discriminative features.

Model Architecture. As shown in Tab. 3, in order to assess the influence of diverse model architectures on our framework, we report the performance outcomes under all cross-combination modes of ResNet-50 (RN) [7] and ConvNeXt-B (CN) [16] with Norm-Aware Embedding (NAE) [3] and Attention Embedder (AE). When comparing backbone networks, ConvNeXt-B significantly outperforms ResNet-50 across all configurations. Furthermore, the AE leads to a further performance improvement compared to the NAE, especially when it is integrated with CN. Notably, the performance gains of AE are relatively less pronounced when combined with RN, suggesting that the compatibility between the backbone network and the embedder affects the performance of the framework. The results indicate that the combination of CN and AE is the optimal configuration.

4 Conclusion

In this paper, we focus on UDA Person Search, where existing methods suffer from noise accumulation caused by source-biased pseudo-labels. To bridge the domain gap, we propose the STCSP framework, which systematically suppresses noise contamination, avoids noise occurrence, and mitigates noise generation. With STC pipeline, IBEM method and SPCL module, our framework achieves SOTA performance. For future research, the IBEM algorithm can be extended to other tasks that demand the rapid completion of extensive bipartite matchings.

Acknowledgments and Disclosure of Funding

This work was supported in part by the National Natural Science Foundation of China Grant 62576067 and 62501226, National Key Research and Development Program of China Grant 2024YFB4710800, Liaoning Provincial Natural Science Foundation Grant 2025-YQ-01 and 2024-MS-012, Dalian Science and Technology Talent Innovation Support Plan Grant 2024RY010, Natural Science Foundation of Hebei Province Grant F2025201037.

References

Mohammed Khaleed Almansoori, Mustansar Fiaz, and Hisham Cholakkal. Ddam-ps: Diligent domain adaptive mixer for person search. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 6688-6697, 2024.

Xiaojun Chang, Po-Yao Huang, Yi-Dong Shen, Xiaodan Liang, Yi Yang, and Alexander G Hauptmann. Rcaa: Relational context-aware agents for person search. In Proceedings of the European Conference on Computer Vision (ECCV) , pages 84-100, 2018.

Di Chen, Shanshan Zhang, Jian Yang, and Bernt Schiele. Norm-aware embedding for efficient person search. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 12615-12624, 2020.

Tianxiang Cui, Huibing Wang, Jinjia Peng, Ruoxi Deng, Xianping Fu, and Yang Wang. Fast one-stage unsupervised domain adaptive person search. In Kate Larson, editor, Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 , pages 713-721. International Joint Conferences on Artificial Intelligence Organization, 8 2024. Main Track.

Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition , pages 248-255. Ieee, 2009.

Chuchu Han, Kai Su, Dongdong Yu, Zehuan Yuan, Changxin Gao, Nong Sang, Yi Yang, and Changhu Wang. Weakly supervised person search with region siamese networks. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 12006-12015, 2021.

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 770-778, 2016.

Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning , pages 448-456. pmlr, 2015.

Yimin Jiang, Huibing Wang, Jinjia Peng, Xianping Fu, and Yang Wang. Scene-adaptive person search via bilateral modulations. In Kate Larson, editor, Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24 , pages 938-946. International Joint Conferences on Artificial Intelligence Organization, 8 2024. Main Track.

Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly , 2(1-2):83-97, 1955.

Junjie Li, Yichao Yan, Guanshuo Wang, Fufu Yu, Qiong Jia, and Shouhong Ding. Domain adaptive person search. In European conference on computer vision , pages 302-318. Springer, 2022.

Zhengjia Li and Duoqian Miao. Sequential end-to-end network for efficient person search. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 35, pages 2011-2019, 2021.

Zongyi Li, Yuxuan Shi, Hefei Ling, Jiazhong Chen, Runsheng Wang, Chengxin Zhao, Qian Wang, and Shijuan Huang. Knowledge consistency distillation for weakly supervised one step person search. IEEE Transactions on Circuits and Systems for Video Technology , 2024.

Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision , pages 2980-2988, 2017.

Hao Liu, Jiashi Feng, Zequn Jie, Karlekar Jayashree, Bo Zhao, Meibin Qi, Jianguo Jiang, and Shuicheng Yan. Neural person search machines. In Proceedings of the IEEE International Conference on Computer Vision , pages 493-501, 2017.

Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 11976-11986, 2022.

Bharti Munjal, Sikandar Amin, Federico Tombari, and Fabio Galasso. Query-guided end-to-end person search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 811-820, 2019.

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems , 32, 2019.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , pages 8748-8763. PmLR, 2021.

Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems , 28, 2015.

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research , 15(1):1929-1958, 2014.

Benzhi Wang, Yang Yang, Jinlin Wu, Guo-jun Qi, and Zhen Lei. Self-similarity driven scale-invariant learning for weakly supervised person search. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 1813-1822, 2023.

Jiabei Wang, Yanwei Pang, Jiale Cao, Hanqing Sun, Zhuang Shao, and Xuelong Li. Deep intra-image contrastive learning for weakly supervised one-step person search. Pattern Recognition , 147:110047, 2024.

Jimin Xiao, Yanchun Xie, Tammam Tillo, Kaizhu Huang, Yunchao Wei, and Jiashi Feng. Ian: the individual aggregation network for person search. Pattern Recognition , 87:332-340, 2019.

Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang. Joint detection and identification feature learning for person search. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 3415-3424, 2017.

Yichao Yan, Jinpeng Li, Shengcai Liao, Jie Qin, Bingbing Ni, Ke Lu, and Xiaokang Yang. Exploring visual context for weakly supervised person search. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 36, pages 3027-3035, 2022.

Yichao Yan, Jinpeng Li, Jie Qin, Song Bai, Shengcai Liao, Li Liu, Fan Zhu, and Ling Shao. Anchor-free person search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 7690-7699, 2021.

Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, and Xiaokang Yang. Learning context graph for person search. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 2158-2167, 2019.

Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, and Qi Tian. Person re-identification in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 1367-1376, 2017.

Zhun Zhong, Liang Zheng, Donglin Cao, and Shaozi Li. Re-ranking person re-identification with kreciprocal encoding. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 1318-1327, 2017.

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Peer reviews:
comments: SUMMARY:
This research presents a customized unsupervised domain adaptive framework specifically designed for person search tasks. The framework achieves this by integrating spatiotemporal coherence analysis and embedding scene-specific prior knowledge into both the detection and re-identification components of the person search workflow. This strategic integration significantly reduces the adverse effects of inaccurate pseudo-labels. Empirical assessments reveal notable performance improvements across two well-established benchmarks in the field of person search.

STRENGTHS AND WEAKNESSES:
Strengths
1) This research presents a pioneering framework tailored for unsupervised domain - adaptive person search. The framework proves to be highly effective, delivering state - of - the - art performance in the field.
2) The concept of integrating the scene prior, which assumes that individuals within a single scene possess unique identities, is both logical and enlightening.

Weaknesses
1) To enhance the readability of the content, Figure 4 could be enlarged to a suitable extent.
2) The author is suggested to modify the width of Table 2 to facilitate the reading process.

QUESTIONS:
1) In the STCSP framework, bipartite matching is executed for instances between any two images to prevent identity collisions. I am perplexed as to why bipartite matching is deemed necessary and why it is applied across any pair of images. Could you provide further insights into this aspect?
2) For the SPCL module, are the LUT and the CQ also re - generated prior to each training epoch, akin to the PT? Moreover, what specific contents do these two components store?

LIMITATIONS:
In practical applications, this technology may involve disputes over personal portrait rights.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-27

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces STCSP, a novel unsupervised domain adaptation  framework for person search that effectively mitigates noise in pseudo labels through a multi-stage consensus mechanism enriched by scene-level priors. Key contributions include:\n\n1. Spatiotemporal Consensus : A dual-filtering pipeline that suppresses detector and re-ID noise using temporal voting across memory banks and spatial checks at the image level, ensuring consistent label quality over time and space.\n2. Iterative Bilateral Extremum Matchin: A GPU-parallelizable clustering algorithm that instantiates the “one-person-per-ID” scene prior, efficiently reconciling identity clusters across large-scale image collections.\n3. Scene Prior Contrastive Learning : A domain-specific contrastive module that reinforces discriminative identity features in the target domain by leveraging cluster-structure regularization.\n4. State-of-the-art performance: STCSP achieves 50.2% mAP on PRW and 87.0% mAP on CUHK-SYSU, significantly outperforming previous UDA methods without relying on ground-truth annotations.\n5. The core idea lies in exploiting spatiotemporal consistency and scene-level semantics to refine pseudo-labels and bridge the domain gap without supervision.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper proposes STCSP, which couples spatiotemporal-consensus voting, a GPU-parallel Iterative Bilateral-Extremum Matching (IBEM) algorithm, and scene-prior contrastive learning. By simultaneously enforcing temporal coherence, a “one-person-one-ID” scene prior, and feature-space regularisation, it systematically suppresses pseudo-label noise and closes the source-to-target gap without any target annotations—offering a structured, scene-aware alternative to prior UDA pipelines such as MMT, CCL, and MEB-Net.\n\n2. The experimental design is rigorous and multifaceted: On the two standard benchmarks the method exceeds the leading UDA baseline (DDAM) by +13.5 mAP / +6.1 top-1 on PRW and +7.5 mAP / +6.6 top-1 on CUHK-SYSU. Extensive ablations—sequentially disabling STC (spatial / temporal), IBEM, and SPCL—cause step-wise drops of up to 3.4 mAP, attributing the final 50.2 mAP / 87.0 mAP to each component with high confidence.\n\n3. The paper is clearly written and well-structured, with detailed figures illustrating the end-to-end data flow.\n\nWeaknesses:\n1. The manuscript does not state whether the ConvNeXt-B backbone is frozen or fine-tuned during adaptation—a factor that affects GPU memory, training time, inference speed, and the fairness of baseline comparisons.\n\n2. Key statistics—FLOPs, GPU-hours, memory-bank growth strategy, update frequency, and parameter counts for IBEM—are absent. Without a quantitative comparison to existing methods, the claimed scalability and real-world deployability remain unverified.\n\n3. Results are confined to PRW and CUHK-SYSU; robustness on cross-city, occlusion-heavy, or multi-camera datasets is unexplored. Moreover, although IBEM is GPU-efficient, its objective overlaps with earlier density- or spectral-based cluster refinements; a direct theoretical or empirical comparison would strengthen the originality claim.\n\nQUESTIONS:\n1. Could you clarify whether the ConvNeXt-B backbone is kept frozen or fine-tuned during the adaptation process? This detail is critical for assessing training complexity, GPU memory consumption, and ensuring fairness in comparison with other UDA baselines.\n2. Can you provide a detailed analysis of the computational efficiency of STCSP, including FLOPs, GPU-hours, memory usage, and parameter counts—particularly for the IBEM and memory-bank modules? A direct comparison with existing UDA methods (e.g., MMT, MEB-Net) would help substantiate the claimed scalability and deployment potential.\n3. Given that the evaluation is limited to PRW and CUHK-SYSU, have you considered testing STCSP on more challenging benchmarks such as Occluded-Duke, CityFlow-PS, or MSMT17? This would help assess the framework’s robustness in more complex or real-world scenarios.\n4. IBEM shares conceptual similarities with existing clustering or noise-refinement methods (e.g., DBSCAN, spectral clustering). Could you elaborate on how your method differs in principle or performance from these approaches? A more explicit theoretical or empirical comparison would help clarify the novelty of your clustering design.\n\nLIMITATIONS:\nThe checklist embedded in the paper explicitly marks the limitations question as answered “No” because space constraints prevented discussion. A key limitation of the current work is that the experimental evaluation is confined to two benchmarks—PRW and CUHK-SYSU—which feature relatively constrained environments with limited diversity in scene layouts and camera networks. The generalizability of the proposed method to more challenging settings, such as cross-city deployments, multi-camera systems, or occlusion-heavy scenarios, remains unexplored. The authors are encouraged to either extend the evaluation to such datasets or explicitly acknowledge this as a direction for future work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes proposes a Spatiotemporal Consensus with Scene Prior (STCSP) framework that effectively eliminates the interference of noise on pseudo-labels, establishes positive feedback, and thus gradually bridging the domain gap. \nSpatiotemporal Consensus (STC): Suppresses pseudo-label noise by integrating temporal consistency and spatial consensus.\nIterative Bilateral Extremum Matching (IBEM): Leverages scene prior to sparsely regularize cross-instance distances using GPU-accelerated bipartite matching, preventing erroneous intra-scene identity assignments.\nScene Prior Contrastive Learning (SPCL): Uses triplet and cross-entropy losses to enforce identity discrimination within scenes, directly learning target-domain knowledge.\nSTCSP achieves SOTA results on PRW and CUHK-SYSU.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The unified spatiotemporal consensus pipeline elegantly mitigates error accumulation in pseudo-labels, a key challenge in UDA person search.\n2. SPCL meaningfully incorporates scene-level discriminative learning beyond proxy-based contrastive losses.\n3. STCSP achieves SOTA results on PRW and CUHK-SYSU.\n\nWeaknesses:\n1. Experiments use only two datasets. Cross-dataset generalization (e.g., CUHK-SYSU→PRW) is unexplored.\n2. The memory bank mechanism lacks analysis of computational overhead. \n3. Lack of related work section\n\nQUESTIONS:\n1. STCSP uses ConvNeXt-B/ResNet-50. Have you tested ViT or Swin backbones?\n2. Table 2 shows that removing the SPCL triplet loss only results in a 0.3% mAP drop (PRW), yet Section 2.5 claims that it effectively reduces noise generation. Is this an overstatement of its contribution?\n3. What is the added training and inference cost (e.g., FLOPs, runtime)\n\nLIMITATIONS:\n1. Memory bank storage and IBEM iterations may hinder edge deployment.\n2. There are fewer ablation experiments, and there is a lack of experiments on training and inference costs, as well as cross-dataset generalization.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
In this work, a person search framework, Spatiotemporal Consensus with Scene Prior (STCSP), is proposed to tackle the noise pseudo label issue in unsupervised learning. By leveraging spatio-temproal consensus procedure and scene prior-based method based on multiple previous approaches, the framework achieves the state-of-the-art performance on PRW and CUHK-SYSU.

STRENGTHS AND WEAKNESSES:
The paper’s writing is coherent and it is easy to follow the work. The motivation for reducing noise on pseudo-labels is clear. Also, comprehensive experiments including an ablation study are properly conducted to showcase the proposed method. 
However, the method appears to be a combination of several prior works, and the main contributions are not clearly distinguished. A more explicit explanation of what is novel and how it differs from existing approaches would be helpful. It might be because I'm not very familar with person search.

QUESTIONS:
Compared to UDA baselines, the performance improvement of STCSP (ResNet-50) is more prominent on PRW than on CUHK-SYSU. Could you explain why?

LIMITATIONS:
There is no limitation part in the paper.
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-03