Spatiotemporal Consensus with Scene Prior for Unsupervised Domain Adaptive Person Search
Authors: Yimin Jiang
Huibing Wang
Jinjia Peng
Venue: NeurIPS 2025 poster ()
Keywords: Person Search
Primary area: general_machine_learning

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
Yimin Jiang' Dalian Maritime University yimin.jiang@dln jiangedlmn edu. en
Huibing Wang* Dalian Maritime University huibing. wangodlmu. edu. en
Jinjia Peng Hebei University
pengj imngjinjaa@hbr edu. cn
Abstract
Person Search aims locate query persons in gallery scene images, but faces severe performance degradation under domain shifts. Unsupervised domain adap- tation transfers knowledge from the labeled source domain 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 Spatiotem- poral Consensus with Scene Prior (STCSP) framework that effectively eliminates the interference noise on pseudo-labels, establishes positive feedback, and thus gradually bridging the domain gap. Firstly, STCSP uses a Spatiotemporal Con- sensus pipeline suppress the noise from being mixed into the pseudo-labels, Secondly, leveraging the scene prior, STCSP employs our designed Iterative Bi- lateral Extremum Matching method to prevent the occurrence of some incorrect pseudo-labels. Thirdly, propose 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-ee performance two benchmark datasets, PRW with 50.2% mAP and CUHK-SYSS with 87.0% mAP.
Introduction
Person search aims to localize and identify a query person from a gallery scene images. can be taken joint task of person detection and re-identification (re-id) in end-to-end manner, where supervised learning has made significant advancements. However, notable performance degradation observed in these methods when deployed to application scenarios, primarily attributed to domain gaps induced by factors such as camera configuration. Additionally, the process of annotating adequate amount of training data for particular domain is both arduous and costly. Consequently, unsupervised domain adaptation (UDA) holds considerable promise in practical real-world scenarios for person search.
UDA person search aims to achieve model generalization from labeled source data to unlabeled target deployment scenarios. DAPS [II] pioneering work in applying UDA person search, employs implicit domain alignment in conjunction with pseudo-labeling method to effectively bridge the gap between the source and target domains. Building upon DAPS, Almansoori al. proposed DDAM (.) which generates a hybrid domain and learns within to minimize the distance between the two domains. Although these methods achieve satisfactory performance, they failed to
"Corresponding Author I "qqual Contribution
Our code is available at/attps://ittaaii httttt/////gthtbb....///a//////m
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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g Peoon
N Prreing
(a) Correct labeling.
(b) Inaccurate BBoxes.
(c) Incorrect Identities.
(d) Identity Conflict.
Figure 1:
Display of incorrect pseudo-labels. (a) A correct pseudo-label has accurate BBox and correct person identity. (b) An inaccurate pseudo-BBox manifests in the form of either encapsulating an erroneous region exhibiting the overlap of multiple boxes. (c) An incorrect identity label comes from situations where an identity is assigned to different individuals or where different instances of person are assigned different identities. (d) Assigning the same identity label intra-scene persons a clear error.
effectively eliminate the interference of noise stemming from the source-biased models on pseudo- labels. As illustrated in Fig. [b] the inaccurate bounding boxes (BBoxes) inevitably lead to detection failures, which can induce significant degradation in person search performance. Additionally, as shown in Fig. Ic the incorrect labels will directly cause confusion in identifying the person identities. Furthermore, the noise accumulates with the increase of epochs, forming negative feedback mechanism that will cause performance degradation of the model during the later training phase. Therefore, eliminating the interference of the noise on pscudo-labels is a major challenge. In addition, previous studies failed to realize that scene can provide relationships for its inner persons. Specifically, the identities of persons within scene image are inherently distinct, and this referred to as scene prior. The omission of considering and exploiting this scene prior results in conspicuous error as illustrated in Fig. Id Consequently, the utilization of the scene prior is another crucial issue.
To address these issues, we propose novel Spatiotemporal Consensus with Scene Prior (STCSP) framework that eliminates the impact of the noise through suppressing noise contamination, prevent- ing 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 suppress noise propagation in pseudo-labels. STC maintains 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. [d. However, deriving the optimal solutions for bipartite matching across thousands images is computationally prohibitive. To tackle this challenge, we propose an Iterative Bilateral Extremum Matching (IBEM) method, leveraging GPU acceleration, seek the approximate solutions. Third, we propose Scene Prior Contrastive Learning (SPCL) module that encourages the model 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 directly absorb knowledge from the target domain. The existence of this knowledge effectively reduces the noise output of the model.
Our major contributions summarized below:
A Spatiotemporal Consensus pipeline proposed to suppress the noise origination from the domain gap, thereby generating reliable baeudo-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-an (SOTA) performance on two benchmark datasets. Specifically, attains a mean average precision of 50.2% on the PRW dataset and 87.0% the CUHK-SYSU dataset.

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Spatiotemporal Consensuss for
Measurement with Scene Prior
Spatiotepporal Consensus
Re-ID
Memory
Last Labels
Spattal Consensus
Temporee Consensus
Bank
Missum
IBEM
Treget
Temporai Consensus
Boots
Embeddings
24x12Map
Concatenate
Person
00:0
Detection Paw
Conv 1-4
Embeider
Atecãion
Calculate Proxies
12xhMap
psample
EMA
PIFO
LUT
CO
GRL + Image Level DD
GRL +
Pull
Pixels
Detection Level DD
Re-ID Level DD
Embedd-
Background
Cross Entrogy
Backbone
Delection
Re-1D
T Task Cooper.
Scene Prior CLL
Figure 2: Architecture of the proposed framework. This figure is structured into two rows. The first row presents the input images and the spatiotemporal consensus pipeline. The second row, arranged from left to right, encompasses the backbone network, detection module, re-id module, task cooperation module, and scene prior contrastive learning module.
2 Method
2.1
Framework Overview
Our UDA person search model is end-to-end architecture based SeqNet [12]. Differently, we replaced the backbone network and person feature embedder with ConvNeXi-B [6] and our designed attention embedder, respectively. Following the approach of DAPS TT] 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 obtain reliable pseudo-labels. The SPCL and task cooperation (TC) modules are employed to steer our model to learn discriminative features.
introduce
Prior to the training of each epoch, the STC pipeline preprocesses the unlabeled target domain. As in the first of Fig. 2 the temporal consensus updates the memory bank with -nnerredneeree BBox scores, BBoxes, and embeddings. The spatial consensus then screens out pseudo- BBoxes from the memory bank. Subsequently. their embeddings calculated by Re-Ranking (0 form cross-distance matrix. This matrix undergoes bipartite matching via the IBEM method, and the result 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 proxy sent to the SPCL.
During the training phase, the model is fed source and target domain mini- mati-batches an alternating sequence, with parameters being updated every 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 then fed into the Faster-RCNN based [20 detection module for BBox generation, where Rol-Align pools the instance feature map for each BBox and the Attention Embedder encodes it into 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-unioo (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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2/ 6
5 6 7
5
6
7
6
7
Figure 3: An example of IBEM. On the left and right are the cost matrix and the optimal solution of a minimum bipartite matching task, respectively. The first BEM extracts the primary pair, and the secondary BEM resolves the residuals.
updated with momentum The matching and updating operations are expressed as follows:
9 E G, & E H. p(g, ht) 0,
و 19 + (1 17)h
argmax p (h, a) = 9, argmax p (g. b) = h aeg
(I)
bE
where G is set of updated anchor BBoxes, p is box IoU function and is threshold for the IoU score. In this equation, the anchor BBox and the estimated BBox h are mutually loU-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 true, consider each anchor 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 independent of all anchor BBoxes used to generate new anchors. Whose definition is as follows:
R = E H
max (g. h) < 0 9Eg
The boxes in Pl with 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 applied to divide H into multiple subsets, as follows:
F Split by NMS,
{ P Flz. }.......
(3)
Specifically, box retained by the NMS and the boxes it suppresses 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 + 9 G is temporal consensus.
Spatial Consensus for Detection. The comprehensive analysis of Eqs. ( to reveals that the maximum value of the IoU scores among the anchor BBoxes in the memory bank approximately 0. This indicates that there may be multiple BBoxes stacked on one person instance, as shown in the right of Fig. lb Therefore, we apply NMS with threshold yo 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, adopt Re- Re-Ranking 30 method compute pairwise distances between instance embeddings. To optimize computational performance, we have developed GPU-accelerated implementation using PyTorch. However, different from previous works, find that scenes prior can sparsely regularize the distance matrix, thus avoiding the error as shown in Fig. [dl Specifically, approach constructs inter-scene bipartite matching to set unmapped pair distances to infinity, thereby enforcing intra-scene identity uniqueness. However, the O (n²) omplexity of exact solvers like Hungarian algorithm [10) becomes prohibitive scale (+s 2hours/ 10k images).
matrices from the instance distances, performing minimum
Iterative Bilateral Extremum Matching, To relieve this dilemma, we propose an IBEM method to calculate the approximate solution. Specifically, shown in algorithm]] [ the IBEM iteratively identifies entries that are extrema (e.g., minima or maxima) both their row and column, then removes the matched entries' rows and columns for further iterations until the conditions are met.

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Algorithm 1 Iterative Bilateral Extremum Matching
Require:
Cost matrix C = [ci,I,"..) } and its row and column indices I1 and J1- Matching pairs: M Mi M2 U... M.
Ensure:
while (- = 1 to 6, [ / [1] |J1| < 0 do
M2 < (i,j) Cij = min C Cijj = min Ci REJ.
kEl,
4+1 f {i E 1 p. (p.q) E MM}
J++1" f- {j E Jelj (p.)).)) Mr}
end while
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 theoretically impossible. Consequently, full instance matching guaranteed via the iterative process. To balance computational efficiency and convergence speed, we introduce two hyperparameters, an unmatched proportion threshold & and a maximum number of iterations 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 spatial clustering consensus strategy, which identifies reliable clusters through suul-granularity pattern analysis. Specifically, we implement a dual- duns-yy DBSCAN strategy: A conservative density with radius c and min sample size x captures coarse- grrseng clusters, while an aggressive density with radius a c and min sample size a K detects fine-grained clusters. Then, through the cluster consensus method, our strategy synthesizes spatial consensus clusters, as shown on the first row of Fig. [ This pross-validatio 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 & in a set E = E2,, ... En) containing n instances, there are two different strategies, & and V, to obtain its cluster labels & (5, =) and vi (E. respec- tively. So, in strategy 4 the cluster which & belongs to is as follows:
F (c. 2 {a E (z. E) 4 ())
Based on F, an intersectionn-ver-umii score of the clusters where is located in o and T can be calculated as follows:
((, f Eg D = (E, UF (g.E.
(5)
where F E, and (c, E, v represent the clusters where E is placed in & and t, respectively. Then, the maximum IoU score among all instances in F (g. 3, ②) is as follows:
g (S, E Φ v)
[ (xx E, V). 2EF(E,E,0)
max
(6)
Finally, a cluster consensus label for & obtained through the following conditional mapping:
④ (E.
fE. Φ. v) g E. क., v) 0.5
h (g,E, V)
-1, else
5

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where E5.
(5, means that lies in the intersection of F (g, Φ) and (C. E, W) which has the maximum IoU score within (E. E, Φ). Moreover, we enforce 0.5 IoU threshold to validate high confidence in t and V consensus. Besides, an instance with cluster label of denotes an outlier.
D.
2.5 Scene Prior Contrastive Learning
Existing works have achieved satisfactory results through contrastive learning using person feature proxies. However, they failed realize and utilize scene prior guide the model learning. Therefore, we design a triplet loss function to encourage the model to learn this prior knowledge. Specifically, introduce person proxy table (PT) V fei, "2,-.. for the target domain. Before each training epoch, the PT will be re- re-formed by taking the mean value of embeddings in each cluster. And, during the training, is updated online with momentum A. Given set I of the instance feature embeddings in a scene image, the margin m of an embedding æ in is calculated as follows:
(UL(=). max x), A {a E Z + (z)),
8
where (---) denotes the inner product between two embeddings and L (.) represents taking the label corresponding to the embedding. *L(x) is the proxy in V corresponding to the æ and A is all the pyper-parammter M to evaluate
embeddings in I that have different labels from x. Besides, the margin. And the triplet loss objective is to increase the margin m to M:
Crriplet = max (M m. 0).
(9)
In addition, we also employ cross-entropy loss in this module. It calculates the probability that the label of x (x), and uses cross-entropy to supervised the probability.
((L(x)).))
CCF logp, p.p, p
8 (0,)+ + Σ E (c,x)) 8 (a, = exp exp((a.b)/ ((a)))/T) (10) cEQ
Σ (a, VEV
+
Σ DED
where r = 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 obtained by adding two terms together:
LSPCL = -rrippeet + LOE.
(11)
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 enhance the foreground features and suppress the background features, thereby improving the model's detection and re- id performance simultaneously. We improved the equation for calculating the probability that an embedding belongs to the foreground as follows:
p = a BN (1m1/22]]
[BN
(12)
where a (-) is a sigmoid function, BN (.) is an batch normalization g layer, and ||5]], is the Lz-norm value of the feature embedding x. We employ the square L2-norm as the regularization term to impose independent constraints each dimension of x and prevent outliers in some dimensions from influencing others through cross-term interactions. And dividing by 2 to simplify the derivative operation. Finally, Focal Loss [14] is calculated for which focus training the hard negatives:
Crc - - pc) log (pt), Pt p.
Pp
==1
(13)
{1
where y is a label (either 0 or 1) indicating whether its corresponding embedding marked background or person.

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Mean
Pos. Encod
Drnoout
Concal.
Sequence
EmboeHlng
Figure 4: Details of the proposed attention embedder. "Pos. Encod,", "Concat." and "Batch Norm."
respectively represent "Positional Encodings", "Concatenate" and "Batch Normalization".
2.7 Attention Embedder
Inspired by the attention pool module in CLIP [9], we propose 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 S (BN) and Dropout 221. BN standardizes
the distribution of each embedding dimension by centering it near zero with unit variance, mitigating
its congregation in positive 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 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, 18,184 images collected from handheld cameras, movies, and TV shows, resulting in significant scene diversity. encompasses 8,432 unique person identities and 96,143 annotated bounding boxes. The dataset is split into 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 gallery size ranging from 50 to 4,000, with 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, 10 annotated bounding boxes. The training set consists of 483 identities with 5,704 images, and the test set contains 2,057 queries and 6, 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.
NVIDIA A800 GPU and implement model with PyTorch [8]. ConvNeXt [16] pre-trained on ImageNet [5] adopted as the backbone network. During training,
Implementation Details. We conduct all experiments
set the batch size to 4, and Automatic Mixed Precision (AMP). Adam 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 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 0 and in Sec. 2.2 are set to 0.7 and 0.4, respectively. For algorithm| the limitations and i are in Sec. 2.4 set to 0.5, 4 and 0.8, respectively. For Eq. (Р). we initialize the hyper-parameter M as 0.35. For the attention embedder, the probability of the Dropout set to 0.1.
2 and 0.2, respectively. The hyper-parameter
and
3.2 Comparison to the State-of-the-Arts
Comparison on PRW. The 3rd column of Tab. I shows the performance of all methods on the PRW test set. As shown in the table, our framework outperforming all the other UDA and weakly- unsupervised methods, whether based on ConvNeXt or ResNet. Especially for the current best- performing DDAM (1. STCSP outperforms it by 13.5% and 6. 1.1% 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.
7

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Table 1: Comparison of mAP(%) and top- accuracy with the fully-supervised, weakly-superviste
and UDA methods on the PRW and CUHK-SYSU test sets. For UDA methods, the best and second best scores are shown bold and underlined, respectively.
PRW
CUHK-SYSU
Method
Backbone
mAP
top-1
mAP
top-1
Fully Supervised Learning
OIM 25
ResNet-500
21.3
49.4
75.5
78.7
IAN
ResNet-50
23.0
61.9
76.3
NPSM 15
ResNet-50
24.2
53.1
77.9
81.2
RCAA 2
ResNet-50
79.3
81.3
CTXG 28
ResNet--50
33.4
73.6
84.1
86.5
QEEPS
ResNet-50
37.1
76.7
88.9
89.1
NAE+ 8
ResNet-50
44.0
81.1
92.1
92.9
AlignPS+ 27
ResNet-50
46.1
82.1
94.0
94.5
SeqNet 12
ResNet-50
46.7
83.4
93.8
94.6
SEAS
ResNet-50
52.0
85.7
96.2
SEAS
ConvNeXI-B
60.5
89.5
97.1
97.8
Weakly Supervised Learning
Net
ResNct-50
21.4
75.2
86.0
87.1
CGPS 26
ResNet-500
16.2
68.0
80.0
82.3
SSL
ResNet-50
30.7
80.6
87.4
88.5
KCD
ResNet-50
40.5
83.6
86.8
88.2
DICL
ResNet-50
35.5
80.9
87.4
88.8
Unsupervised Domain Adaptation
DAPS
ResNet-50
34.7
80.6
77.6
79.6
FOUS
ResNNct-50
35.4
80.8
78.7
80.5
DDAM
ResNet-50
36.7
81.2
79.5
81.3
STCSP (ours)
ResNet-50
42.4
82.1
80.4
82.5
STCSP (ours)
ConvNeXt-B
50.2
87.3
87.0
87.9
90
mAPP)))
cop-1 (9%)
STCSP
STCSP(RN)
STCNP(RN)
DDAM
DDAM
DAPS
DAPS
100
500 1000 4000
100
500 1000
Gallery Size
Gallery Size
(a) Comparison of mAP.
(b) Comparison of top-1 accuracy.
Figure 5: Comparison of mAP and top-1 accuracy on CUHK- SYSU across various gallery sizes.
Comparison CUHK-SYSU. The performance of our method and the other methods the CUHK-SYSU test set shown in the 4th column Tab. 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 top- accuracy, surpassing DDAM I1 by 7.5% and 6.6% in mAP and top-1 accuracy, respectively. The CUHK-SYSU training set boasts greater diversity and larger quantity of scene images compared to the PRW training set. As result, UDA from PRW to CUHK-SYSU presents a formidable challenge. Despite this, 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 -SSU test set with gallery sizes ranging from 50 to 4,000. Fig-5 showss the performance curve of all the methods in terms of mAP and top-1 the gallery size increases. Since is challenge for all compared methods consider more distracting persons in the gallery set, the performance of them reduced the gallery size increases. However, method consistently outperforms all the other methods in

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Quaryyy
BATTS
DIAFS
DAPS
DAATS
Ous
Figure 6: Qualitative comparison of our method with DAPS on the PRW test set. Each column represents comparison result. The blue bounding boxes denote the queries, while the green and red bounding boxes denote correct and incorrect top-1 matches, respectively.
Table 2: Comparative results of the different strategies for our framework.
PRW
CUHK-SYSU
Strategy
mAP
top-1
mAP
top-1
w/o Detection Spatial Consensus
46.8
85.7
85.4
85.4
w/o Detection Temporal Consensus
47.7
86.0
86.4
87.5
w/o Re-ID Spatial Consensus
46.9
85.8
86.7
87.7
w/o Re-ID Temporal Consensus
49.0
87.4
86.6
87.5
w/o IBEM
48.2
85.9
85.9
86.6
w/o Triple Loss of SPCL
47.9
87.0
86.2
87.4
50.2
87.3
87.0
87.9
mAP and top-1, whether based ConvNeXt or ResNet. This indicates that our method has excellent generalization ability.
Some example person search results 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 scene, the search results illustrated in the 1st and 4th columns. The 2nd column shows the result of the person's 's body being obstructed. The 3rd and 6th columns show the result when the scene and viewpoint change. It can be observed that successfully handles low-resolution, similar-character, occlusion, cross-scene and viewpoint variation, while other state-of-the-art methods such DAPS fail in these scenarios. This demonstrates the robustness of our method.
Qualitative Results.
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 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. 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 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 Tab, 2 that the temporal consensus improve the performance of our model
Through comparison,

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Table Comparative results of the different components for our framework. RN, CN, NAE and AE indicate ResNet-50, ConvNeXt-B, Norm-Award Embedding and Attention Embedder, respectively.
Backbone
Embedder
PRW
CUHK-SYSU
NAE
top-1
mAP
top-1
X
41.1
82.2
80.3
82.3
X
V
42.4
82.2
80.4
82.5
V
J
X
45.8
85.6
85.6
85.5
X
X
V
50.2
87.3
87.0
87.9
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 the table, when IBEM absent from our framework, entailing the potential occurrence of the error depicted in Fig. [d] the performance of our framework experiences a substantial decline across both datasets. Notably, this decline is more pronounced on the PRW dataset. Similarly, indicated in the 6th row of the table, 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 high similarity within dataset. This indicates that scene prior effectively steers the model to learn discriminative features.
scene images in this
Model Architecture. As shown in Tab. 3 in order to assess the influence of diverse model ar- chitectures on our framework, we report the performance outcomes under cross-combination modes of ResNer-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 further perfor- mance improvement compared to the NAE, especially when it is integrated with CN. Notably, the performance gains of AE 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 lseudo-labels. 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.
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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 2024YFB47 10800. Liaoning Provincial Natural Science Foundation Grant 2025-YQ-07 and 2024-MS-01 Dalian Sci- ence and Technology Talent Innovation Support Plan Grant 2024RY010, Natural Science Foundation of Hebei Province Grant F2025201037.
References
[1] 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.
[2] Xiaojun Chang, Po- Yao Huang, Yi-Dong Shen, Xiaodan Liang, Yi Yang, and Alexander G Hauptmann. Reaa: Relational context-aware agents for person scarch. In Proceedings of the European Conference on Computer Vision (ECCV), pages 84- 100, 2018.
[3] Di Chen, Shanshan Zhang, Jian Yang, and Bernt Schiele. Norm-aware embedding efficient person search, Proceedings of the IEFE/CVF conference on computer vision and pattern recognition, pages 1261,-12624, 2020.
[4] Tianxiang Cui, Huibing Wang, Jinjia Peng, Ruoxi Deng, Xiamping 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, IJCA/-24, pages 713-721, International Joint Conferences on Artificial Intelligence Organization, 2024, Main Track.
[5] Deng, Wei Dong. Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei. Imagenet: large-scale hicrarchical image datahase. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. Teee, 2009.
[6] 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.
[7] 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 pottern recognition, pages 770-778, 2016.
[8] Sergey loffe and Christian Szegedy. Batch normalization: Accelcrating deep network training by reducing internal covariate shift. In International conference machine learning, pages 448 4-66.. pmir, 2015.
[9] Yimin Jiang, Huibing Wang, Jinjia Peng. Xianping Fu, and Yang Wang. Scene Scene-adaptee adaptive person search via bilateral modulations. In Kate Larson, editor. Proceedings of the Thirty- Third International Joint Con- ference on Artificial Intelligence, IJCAI-24, pages 938-946. International Joint Conferences on Artificial Intelligence Organization, 2024. Main Track.
[10] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1 211-2):83-4 97, 1955.
[11] 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.
[12] Zhengjia Li and Duoqian Miao. Sequential end-to-end network for efficient person search. Proceedings of the AAA/ Conference on Artificial Intelligence, volume 35, pages 2011 10199, 2021.
[13] 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 Circuits and Systems for Video Technology. 2024.
[14] 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.
[15] 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 503-501,220 2017.
11

---

[16] 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 puttern recogmition, pages 1 1976-11986, 2022.
[17] Bharti Munjal, Sikandar Amin, Federico Tombari, and Fabio Galasso. Query-guider end-to-end person search. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 811 1111-22,2222 2019.
[18] 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 perfforrree deep learning library. Advances in neural information processing systems, 32, 2019.
[19] Alec Radford, Jong Wook Kim, Chris Hallacy. Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transfcrable visual models from natural language supervision. In International conference machine learning, pages 8748 -8663. PmLR, 2021.
[20] Shaoqing Ren, Kaiming Hc, Ross Girshick, and Jian Sun. Faster r-cnn: with region proposal networks. Advances neural information processing systems, 28, 2015.
Towards real-time object detection
[21] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929-1958 2014.
[22] Benzhi Wang, Yang Yang, Jinlin Wu, Guo-jun Qi, and Zhen Lei. Self-similarity driven scale-invariant lcarning for weakly supervised person search. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 181 1813-1822, 2023.
[23] 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, 147:1 10047, 2024.
[24] Jimin Xiao, Yanchun Xie, Tammam Tillo, Kaizhu Huang. Yunchao Wei, and Jiashi Feng. lan: the individual aggregation network for person search. Pattern Recognition, 87:332-340, 2019.
[25] Tong Xiao, Shuang Li, Bochao Wang. Liang Lin, and Xiaogang Wang. Joint detection and identification feature learning for person scarch. In Proceedings of the IEEE conference on computer vision and pottern recognition, pages 3415-3424, 2017.
[26] Yichao Yan, Jinpeng Li, Shengcai Liao, Jie Qin, Binghing 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.
[27] Yichao Yan, Jinpeng Li, Jie Qin, Song Bai, Shengeai Liao, Liu, Fan Zhu, and Ling Shao. Anchor-frec person search. In Proceedings the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7690-7699, 2021.
[28] Yichao Yan, Qiang Zhang, Bingbing Ni, Wendong Zhang, Minghao Xu, and Xiaokang Yang. Learning context graph for person search. In Proceedings of the IEEE/CVE conference on computer vision and pattern recognition, pages 2158-2167, 2019.
[29] 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.
[30] Zhun Zhong, Liang Zheng, Donglin Cao,, and Shaozi Li. Re-ranking person re-identification with k- reciprocal encoding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1318 1327, 2017.
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Reviews:
[{"review_id": "azTcI90LmK", "reviewer": "Reviewer_WfPm", "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.", "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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 2, "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. \nHowever, 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.", "was_revised": true, "final_justification": "After reviewing author's responses and other reviewers' comments, I think the author resolved my major concerns. Therefore, I decided to raise my rating score.", "rebuttal": "_To Reviewer_ __WfPm__:\n\n__Q1:__ Compared to UDA baselines, the performance improvement of STCSP (ResNet-50) is more prominent on PRW than on CUHK-SYSU. Could you explain why?\n\n__A1:__ Thanks for this comment. The differential performance gains on PRW and CUHK-SYSU can be attributed to two key factors related to dataset characteristics and model design:\n\n- CUHK-SYSU is a larger dataset than PRW, with a greater number of images, identities, and scenes. The results on PRW are obtained through supervised-to-unsupervised transfer learning from CUHK-SYSU to PRW, which is a process of transferring from a large dataset to a small one. Our method achieves more significant performance improvements on PRW, demonstrating that it has a stronger knowledge transfer ability than other methods.\n\n- Since the images in PRW were taken on campus, some people wear the same clothing (such as activity T-shirts). Therefore, the pseudo-labels generated for PRW contain more noise than those for CUHK-SYSU, and our STCSP can effectively suppress this noise. This explains why the model's improvement on PRW is more pronounced.\n\n__Q2:__ More explicit explanation of the novelty.\n\n__A2:__ Thanks for this comment. Our method aims to effectively eliminate the interference of noise on pseudo-labels, establish positive feedback, and thus gradually bridge the domain gap for the UDA person search task. Different from previous works, we comprehensively consider the importance of both spatio and temporal consensus in the detection and re-identification stages, which establishes a complete consensus framework to suppress the noise originating from the domain gap and acquire knowledge from the target domain. Moreover, 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, thereby improving the performance of the model with small computational cost."}, {"review_id": "H7fO1rT0UW", "reviewer": "Reviewer_4Hgy", "summary": "This 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.", "questions": "1. 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)", "limitations": "1. 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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", "was_revised": true, "final_justification": "I will keep my positive score", "rebuttal": "_To Reviewer_ **4Hgy**:\n\n**Q1:**  STCSP uses ConvNeXt-B/ResNet-50. Have you tested ViT or Swin backbones?\n\n__A1:__ Thanks for this comment. ConvNeXt-B and ResNet-50 are widely adopted backbone networks for the person search task. Especially, through downsampling stages, ConvNeXt-B builds multi-scale representations, capturing both global body structure and local discriminative cues, which are essential and necessary for distinguishing similar-looking pedestrians. Therefore, in Table 1, it is obvious that STCSP (ConvNeXt-B) achieves the best performance.\n\nConsidering your suggestion, we conduct experiments using Swin-Transformer-Base as the backbone and report the results in the following table. It is observed that after adopting Swin-B as the backbone, the performance is reduced. This is because the local receptive field of ConvNeXt-B can better maintain feature stability in the person search task, whereas the global information interaction in Swin-B may amplify the impact of noise. Therefore, ConvNeXt-B is chosen to be the backbone in our method.\n\n| Backbone   | PRW (mAP) | PRW (top-1) | CUHK-SYSU (mAP) | CUHK-SYSU (top-1) |\n|------------|:--------:|:----------:|:--------------:|:--------------:|\n| Swin-B     | 33.1%     | 79.3%      | 72.8%           | 72.8%           |\n| ConvNeXt-B | 50.2%     | 87.3%       | 87.0%           | 87.9%           |\n\n\n\n__Q2:__ 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?\n\n__A2:__ Thanks for this comment. The triple loss function is designed to incorporate scene prior knowledge into the original contrastive loss, which mitigates the generation of noise (lines 176-178). This serves as a complement to the SPCL module in this work. Moreover, from Table 2, \"SPCL w/o Triple Loss\" reports the results of our method without using the triplet loss. By comparing it with STCSP in the last row, the mAP decreases by 2.3%, and the top-1 decreases by 0.3% on PRW. Since the role of the triplet loss is mainly to increase the similarity gap between different persons, its effect on improving \"mAP\" related to similarity ranking is stronger than \"top-1\" related to the rank-1 accuracy. Therefore, the proposed triplet loss in SPCL indeed enhances the discriminative capability of different persons.\n\n**Q3:** What is the added training and inference cost (e.g., FLOPs, runtime)\n\n**A3:** Thanks for this comment. Below, we provide a comparison of training time, inference speed, and GPU memory usage under the same experimental settings (1 x A800-80G) with an existing UDA person search method DAPS [1]. From the results, we can observe that our method incurs a relatively small cost but significantly enhances accuracy. \n\n| Method | Training time | Inference speed | GPU usage | PRW (mAP, top-1) |\n| ------ | :-----------: | :-------------: | :---: | :-------------: |\n| DAPS [1]   |   ≈20hours    |  ≈102images/s   | ≈45GB |   34.7%, 80.6%    |\n| STCSP  |   ≈21hours    |   ≈96images/s   | ≈50GB |   50.2%, 87.3%    |\n\nIn addition, we have provided the computational costs required by the proposed modules during the training process, as shown in the following table. \"Spatiotemporal Consensus for Detection\" requires reasoning on images from the target domain and updating the memory bank, resulting in a relatively high computational cost. 'IBEM' can process 10k images in just 10 seconds, which also demonstrates its ability to accelerate the training process. 'Spatiotemporal Consensus for Re-ID' is proposed to optimize the clustering process, and no GPU is utilized during clustering. Therefore, it does not incur additional GPU consumption.\n\n| Module | Runtime (10k images) | GPU usage (10k images) |\n| --- | :-: | :-: |\n| Spatiotemporal Consensus for Detection | ≈ 25 minutes | ≈ 30 GB |\n| IBEM | ≈ 10 s | ≈ 15 GB |\n| Spatiotemporal Consensus for Re-ID | ≈ 20 s | None |\n\n**References**\n\n[1] Junjie Li, Yichao Yan, Guanshuo Wang, Fufu Yu, Qiong Jia, and Shouhong Ding. Domain adaptive person\nsearch. In European conference on computer vision, pages 302–318. Springer, 2022."}, {"review_id": "jcBNhSwm3G", "reviewer": "Reviewer_H4P2", "summary": "The 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.", "questions": "1. 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.", "limitations": "The 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.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Most of my concerns have been addressed by the authors, I will maintain the initial score.", "rebuttal": "_To Reviewer_ **H4P2**:\n\n__Q1:__ 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.\n\n__A1:__ Thanks for this comment. Following previous works, the backbone (ConvNeXt-B or ResNet-50) is fully fine-tuned during domain adaptation, with all layers unlocked except for the initial patch embedding layer. When compared with existing UDA baselines (e.g., DAPS and DDAM), as shown in Table 1, the same experimental settings are employed across all evaluations, ensuring the fairness in the comparative analysis.\n\n__Q2:__ 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.\n\n__A2:__ Thanks for this comment. Below, we provide a comparison of training time, inference speed, and GPU memory usage under the same experimental settings (1 x A800-80GB). In addition, executing IBEM takes approximately 10 seconds and requires approximately 15GB of VRAM, which is released upon completion. The memory bank requires approximately 300MB of CPU memory.\n\n| Method | Training time | Inference speed | GPU usage  | PRW (mAP, top-1) |\n| ------ | :-----------: | :-------------: | :---: | :-------------: |\n| DAPS[1]   |   ≈20hours    |  ≈102images/s   | ≈45GB |   34.7%, 80.6%    |\n| STCSP  |   ≈21hours    |   ≈96images/s   | ≈50GB |   50.2%, 87.3%    |\n\nPerson search can be regarded as a joint task of pedestrian detection and person re-id, but MMT and MEB-Net are methods for person re-id, and their papers do not provide data related to computational efficiency. But we add the comparison of the UDA person search method, such as DAPS[1]. As shown in the table above, it is obvious that STCSP has a significant performance advantage, while its computational cost and inference speed are comparable.\n\n__Q3:__ 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.\n\n__A3:__ Thanks for this comment. PRW and CUHK-SYSU are two of the most widely adopted benchmarks in the person search task. CUHK-SYSU is collected from handheld cameras, movies, and TV shows, and PRW is composed of video frames captured by six fixed cameras. They encompass images with diverse real-world challenges, such as occlusion and illumination variations. Moreover, the experimental results (Tables 1, 2, and Figure 6) validate the generalizability of our STCSP, and Figure 5 demonstrates that our method maintains excellent search performance even as the gallery size increases. \n\nOccluded-Duke, CityFlow-PS, and MSMT17 are all person re-identification datasets. Our person search method requires the use of scene images for learning, so we cannot conduct experiments on these datasets. Considering your suggestions, we have added the results of our model on the low-resolution and occluded test set of CUHK-SYSU, demonstrating that our model can effectively handle these real-world challenges.\n\n| Test set       | mAP  | top-1 |\n| -------------- | :--: | :---: |\n| Low-resolution | 76.6% | 79.3%  |\n| Occluded       | 72.8% | 71.7%  |\n\n__Q4:__ 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\n__A4:__ Thanks for this comment. 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, IBEM 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. Moreover, the O(n^3) complexity of exact solvers like Hungarian algorithm[2] becomes prohibitive at scale. Therefore, IBEM is designed to include only extremum operation, which can effectively use the parallel computing power of the GPU to complete the matching between all images in a few seconds. The following is a comparison of efficiency.\n\n| Method    |      Efficiency       |\n| --------- | :-------------------: |\n| Hungarian[2] |  ≈2hours/10k images   |\n| IBEM      | ≈10seconds/10k images |\n\nFurthermore, our method utilizes matrices generated by IBEM for clustering. Our clustering algorithm (Section 2.4) is a spatiotemporal consensus-based approach derived from DBSCAN, and it shares the same time and space complexity as DBSCAN, resulting in a minimal difference in computational cost. The comparison below demonstrates the superior performance of our method.\n\n| Method         | PRW (mAP) | PRW (top-1) |\n| -------------- | :-------: | :--------: |\n| DBSCAN         |   46.5%    |    85.5%    |\n| Our clustering |   50.2%    |    87.3%    |\n\n**References**\n\n[1] Junjie Li, Yichao Yan, Guanshuo Wang, Fufu Yu, Qiong Jia, and Shouhong Ding. Domain adaptive person\nsearch. In European conference on computer vision, pages 302–318. Springer, 2022.\n\n[2] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly,\n2(1-2):83–97, 1955."}, {"review_id": "tTvx6kFWXN", "reviewer": "Reviewer_F6Vt", "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.", "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?\n2) 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.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n1) 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.\n2) The concept of integrating the scene prior, which assumes that individuals within a single scene possess unique identities, is both logical and enlightening.\n\nWeaknesses\n1) To enhance the readability of the content, Figure 4 could be enlarged to a suitable extent.\n2) The author is suggested to modify the width of Table 2 to facilitate the reading process.", "was_revised": false, "final_justification": "", "rebuttal": "_To Reviewer_ **F6Vt**:\n\n__Q1:__ 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?\n\n__A1:__ Thanks for this comment. Bipartite matching utilizes the scene prior (line 42-45), which can avoid the occurrence of some erroneous labels during the generation of pseudo labels. By utilizing bipartite matching, it is possible to ensure that each person can only match one instance in a scene image, thus avoiding the error shown in Figure 1d, where multiple instances share the same identity in a scene image.\n\n__Q2:__ 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?\n\n__A2:__ Thanks for this comment. The LUT and the CQ are used to store proxies and instances in the source domain, and unlike the PT, they are not re-generated or reset before each training epoch. Specifically, the LUT stores identity proxies of persons with identities in the source domain and updates them online with a momentum of 0.5; the CQ is a queue that stores unidentified instances in the source domain and is updated online with a FIFO rule. In addition, since the LUT and the CQ were proposed in previous work[1], they are not introduced in detail in this paper.\n\n**References**\n\n[1] Tong Xiao, Shuang Li, Bochao Wang, Liang Lin, and Xiaogang Wang. Joint detection and identification\nfeature learning for person search. In Proceedings of the IEEE conference on computer vision and pattern\nrecognition, pages 3415–3424, 2017."}]

Author rebuttal:

Meta-review: This paper proposes a spatiotemporal consensus framework with a scene prior for unsupervised domain adaptive person search. Overall, the reviewers acknowledged the merits of the paper, including its novel integration of spatiotemporal consensus mechanisms and scene priors, as well as the state-of-the-art performance achieved on standard benchmarks. After rebuttal, all reviewers raised or maintained their positive ratings as the authors adequately addressed concerns regarding computational efficiency, methodological clarity, and experimental validation. The Area Chair recommends acceptance based on the technical novelty and strong empirical results, but encourages the authors to incorporate all reviewers' suggestions in the final version.
Decision: Accept (poster)