BMW: Bidirectionally Memory bank reWriting for Unsupervised Person Re-Identification
Authors: Xiaobin Liu
Jianing Li
Baiwei Guo
WenbinZhu
Jing Yuan
Venue: NeurIPS 2025 poster ()
Keywords: Memory Bank
Unsupervised Person Re-Identification
Primary area: deep_learning
TL;DR: We propose a new perspective to analyse the memory bank rewriting, and introduce a new inter-class constraint.

Abstract: Recent works show that contrastive learning based on memory banks is an effective framework for unsupervised person Re-IDentification (ReID). In existing methods, memory banks are typically initialized with cluster centroids and rewritten with positive samples via the momentum mechanism along with the model training. However, this mechanism solely focuses on the intra-class compactness by pulling memory banks close to positive samples, neglecting the inter-class separability among different memory banks. Rewriting memory banks with partial constraint limits their discrimination capacities, and hence hinders learning discriminative features based on those memory banks. In this paper, we claim that memory banks should be rewritten with both intra-class and inter-class constraints, and therefore propose a unified memory bank rewriting mechanism, Bidirectionally Memory bank reWriting (BMW), to chase enhanced discrimination capacity. Specifically, BMW formulates the memory bank rewriting as the gradient descent update with two objectives, i.e., reducing intra-class diversity and enhancing inter-class separability. To effectively enhance the separability of memory banks with limited number of rewriting steps, we further design a novel objective formulation for the inter-class constraint, which is more effective for one step update. BMW enhances both representation and discrimination capacities of memory banks, thus leads to an effective ReID feature optimization. BMW is simple yet effective and can serve as a new paradigm for person ReID methods based on memory banks. Extensive experiments on standard benchmarks demonstrate the effectiveness of our BMW method in unsupervised ReID model training. Specially, BMW even outperforms previous methods that use stronger backbones.  Code is available at https://github.com/liu-xb/BMW.

BMW: Bidirectionally Memory bank re Weritingg for Unsupervised Person Re-Identification
Xiaobin Liu Nankai University Tianjin, China liuxb@nankai.cm edw..iiedd..oo
Jianing Li Hong Kong Polytechnic University Hong Kong, China tensor li@polyyu edu. yd.
Baiwei Guo Huawei Technologies Ltd.
Shanghai, China baiwei guo@epf 1. ch
Wenbin Zhu
Jing Yuan'
Nankai University
Nankai University
Tianjin, China
Tianjin, China
wenbinzhudmal nankai. lark.iiedu..oom cn
nkyuanj ing@gma com
Abstract
Recent works show that contrastive learning based memory banks effective framework for unsupervised person Re-IDentification (ReID). In existing methods, memory banks are typically initialized with cluster centroids and rewritten with positive samples via the momentum mechanism along with the model training. However, this mechanism solely focuses on the intra-class compactness by pulling memory banks close to positive samples, neglecting the inter-class separability among different memory banks. Rewriting memory banks with partial constraint limits their discrimination capacities, and hence hinders learning discriminative features based on those memory banks. In this paper, we claim that memory banks should be rewritten with both intra-class and inter-class constraints, and therefore propose a unified memory bank rewriting mechanism, Bidirectionally Memory bank re reiriting (BMW), to chase enhanced discrimination capacity. Specifically, BMW formulates the memory bank rewriting as the gradient descent update with two objectives, i.e., reducing intra-class diversity and enhancing inter-class sep- arability. To effectively enhance the separability of memory banks with limited number of rewriting steps, further design novel objective formulation for the inter-class constraint, which more effective for one step update. BMW en- hances both representation and discrimination capacities of memory banks, thus leads to an effective RelD feature optimization. BMW is simple yet effective and serve
paradigm for person RelD methods based memory banks. Extensive experiments on standard benchmarks demonstrate the effectiveness BMW method in unsupervised RelD model training. Specially, BMW even outperforms previous methods that use stronger backbones. Code is available at https///tthubbccm///iixxbBBW
Introduction
Person Re- Re-Identification (RelD) aims match query person image in gallery set, making it an important task in intelligent surveillance systems [T44]. Currently, supervised person ReID methods have been widely studied from different aspects and achieved promising performance [5]- 14]. However, supervised methods requires laborious manual person ID annotation, limiting their applications in real-world scenarios lacking annotated data. To address this issue, recent researchers
"Coneeponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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w/o inter-class constraint
w/ inter-class construint
+
intra-class constraint
inter-class constraint
Figure 1: Illustration of the memory bank update without (w/o) and with (w/) the inter-class constraint. Dots and stars denote features and memory banks. Different colors denote different clusters. Without the inter-class constraint, memory banks are pulled to approach positive samples only (shown as yellow arrows). With the inter-class constraint, memory banks also push away from each other (shown as purple arrows), thus enhancing their separability.
focus unsupervised person ReID,, and multiple methods are proposed from different aspects, such as pseudo label prediction [15] [6], unsupervised metric learning [7 and image synthesis [19 20)
More details of related works are summarized in Sec. 2
Currently, state-of-the-arr unsupervised person ReID methods adopt the contrastive learning frame- work based memory banks [17 26] These methods typically use the centroid of each cluster obtained by unsupervised clustering methods to initialize the memory bank. During model training, those methods rewrite memory banks with training samples of corresponding clusters by the momen- tum mechanism, i.e., rewritten memory banks are weighted summation of the initial ones and positive samples. Features are optimized by contrastive loss maximize the similarities with corresponding memory banks and minimize the similarities with others.
Despite the significant success of those methods, there still remain two open issues unexplored. Firstly, as memory banks serve as the anchors for the feature optimization, they are required to be both intra-class representative and inter-class separative supervise the discrimination capacities of features. However, the previously used momentum mechanism for memory bank rewriting solely focuses learning representative memory banks for each class, neglecting the inter-class constraint on memory banks. This limits the separability inside memory banks, hence hinders effective feature optimization base those memory banks. Secondly, compared with model parameters that are entirely updated in each iteration, only several memory banks corresponding to selected clusters rewritten once in each iteration. This requires effective objective formulation to enhance the separability with few rewriting steps. Existing metric learning methods commonly formulate the inter-class separability constraint enlarging the Euclidean distance. However, this always leads to larger feature norms, which is ineffective for memory bank rewriting after L2 normalization.
This paper is motivated to study an effective memory bank rewriting mechanism for the unsupervised person RelD model training. Different from existing methods, we innovatively formulate the memory bank rewriting as the gradient decent update for the objective function. From this respective, the previously used momentum mechanism corresponds to the objective function containing only the intra-class constraint for the representability, thus limits the separability of memory banks. To handle this issue, this paper complement the objective function with inter-class constraint to additionally ensure the discrimination capacity of memory banks. As shown in Fig. [ the extra inter-class constraint enhances the separability of memory banks, thus it further enables effective feature optimization. We further propose effective objective formulation for the inter-class constraint by pulling a memory bank close to the opposite vector of memory banks for other classes We show that this is more effective in enhancing separability for one step update. A dynamically weighting strategy is also designed to ensure the awareness of importance of different constraint component.
Our method jointly rewrite memory banks with the above two constraints. We hence call our method as Bidirectionally Memory bank Writing (BMW). We test our BMW on standard benchmarks including M5rket-1501 and MSMT17. Comparison with recent works shows that our BMW is able to enhance the discrimination capacity of features learned with memory bank. For instance, BMW boosts the mAP accuracy from 81.2% 86.3% on Market- outperforming existing both unsupervised and transfer learning methods by a clear margin. Moreover, B.WW even outperforms recent DCC (23] which uses stronger backbone, i.e., 86.2% by BMW with ResNet50 e.s. 85.8% by DCC with ResNet50-ibn on Market-1501.
The contributions of this paper can be summarized as follows:

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1) To the best of our knowledge, this is the first effort analyse the memory bank rewriting mechanism from the perspective of gradient decent update, which could provide certain insight for future studies on methods based on memory banks.
2) For updating memory banks by the gradient decent strategy, we proposes the Bidirectionally Memory bank reWriting (BMW) method which involves both intra-class and inter-class constraints in the objective formulation. And a new objective formulation for the inter-class constraint is proposed to ensure effective update of memory banks in limited steps.
3) BMW enhances both the representation and discrimination capacities of memory banks and thus can boost the feature optimization. Experimental results with superior performance against state-of-the-art methods demonstrate the effectiveness of BMW.
2 Related Work
Domain adaptive person ReID uses both a labeled source dataset and the unlabeled target dataset for model training and aims at transferring knowledge contained in labeled data the target data, Some works targeting to narrow the domain gap between the source data and the target data in image For example, Liu et al. (30 propose an adaptive transfer network to effectively transfer images from label domain to unlabeled domain. Several works try to bridge domain gaps by mapping labeled and unlabeled images to a shared feature space [32 33) However, these methods always require labeled source data for training, which is hardly available due to privacy issue in real-world applications. Compared with them, this paper only uses unlabeled target data, thus is more suitable for real-world applications.
space by GANs (7 28 29
20
Unsupervised person ReID only requires the unlabeled target data for training and has been studied from two main perspectives, i.e.. pseudo label prediction and unsupervised optimization. Some works locally predict pseudo labels for unlabeled person images [2 34 For example, Yu et al. 34 select positive samples in training batches. However, local label prediction is not precise and will mislead the distance optimization. To acquire reliable pairs, some works have designed unsupervised clustering methods [15] [6] and re-rank method (35]. Based on assigned labels, researchers propose
multiple metric learning methods for the unsupervised model training [36 37 34
18]
38
39
21].
For example, Liu et al. [37] propose graph consistency constraint between student models and momentum average models for unsupervised model training. Some works adopt additional cues to
boost performance 45) For examples, Fu et al. [433. Zhang al. 44] and Cho et al. [45] use
local features to boost the performance. While, experiments show that our model outperforms these methods by a clear margin even without additional cues.
Memory bank based methods are previously proposed for unsupervised feature learning 46 48 They use memory banks to store features of all features selected features for the contrastive loss computation. Currently, memory bank based methods dominate the unsupervised person ReID task and achieve promising performance [21 25
[8]
17
22
49
50
51 24]. For examples, 23
Zhong et al. [21] (25). Liu et al. [18] and Wang et al. 52 store each image features in a memory bank for metric learning. Dai et al. uses store centroids of clusters in the memory bank, which is followed by many works 2 35]. Some researchers propose to store different features in memory banks [17 49 51 24 For instance, Ge et al. (17) propose a hybrid memory bank that sore both centroids and outliers. However, these methods only use intra-class positive training samples to update corresponding memory banks. They neglect the inter-class constraint among different memory banks, limiting the separability of them and hindering effective feature optimization.
Compared with existing memory bank based methods, this paper proposes a innovative perspective for memory bank rewriting, i.e., the gradient descent update for the objective function. Guided by this perspective, we propose to combine both the intra-class and inter-class constraints in the objective function, i.e., Bidirectionally Memory bank re Writing (BMW). Experiments show that the proposed BMW outperforms existing methods by clear margin.
3 Overview
Given an unlabeled dataset D, unsupervised person RelD aims learn the RelD model on D without annotation. D can be denoted as {z.li I...N. 2; and N denote the i-th image and the number
3

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of images in D, respectively. We denote the feature extraction ʃ; (1 0). + and denote the CNN feature extractor and its parameters, respectively.
We adopt the contrastive learning framework based memory bank for the unsupervised training on D. Pseudo ID labels on 'D can be generated by unsupervised clustering methods before model training. After clustering features into C' clusters, centroid feature for each cluster is computed by averaging all features in this cluster, resulting in C' centroids corresponding to C' clusters. These centroids for each cluster used to initialize memory bank M,, i.e., M[c] is initialized by the centroid feature of the c-th cluster with L2 normalization.
Via treating M non- paraaetraetic classifier, the contrastive loss is used for model training following For x; belonging to the e-th cluster, the contrastive loss is computed as: previous works 23 22 35
exp(f: M(c//T)
Co(x;) - log Σー exp((f Mb]//T
(1)
where 7 is temperature hyper-parameter to adjust the scale of similarity distribution. Along with the model training, MM is rewriting based both training samples and itself by the proposed BMW method, which will be given in Sec. 4
Bidirectionally Memroy bank reWriting
Existing methods rewrite M with positive training samples via the momentum mechanism. For example, it rewrites MMc] with fi belonging to the e-th cluster can be formulated as:
Mid + aMic + - (1-a)f:-
where a is used to adjust the update rate memory banks.
In this paper, propose novel perspective of memory bank rewriting, which formulates the rewriting procedure as the gradient decent update for objective function LM as follows:
M(d +- M(d] dL
(3)
a.Mcc]
Note that the update rate is omitted in Eqn. and will be used for each constraint of L To enhance both the representation and the discrimination capacities of M, the proposed BMW formulates L. as the combination of both the intra-class constraint Lintra and the inter-class constraint Linter as:
LM Cintra + Xinter Cinters
(4)
where Aintra and Ainter denotes loss weights. Then Eqn. 3 can be rewritten as:
Ceintra(Moa Ainter
(
Mid + Mld Xintra
(5)
0M[c
0Mic
To enhance the rewriting efficiency, the hard sampling strategy is used to compute LM, i.e., the farthest positive sample each training batch and the closest negative memory bank vector for M[c are used to compute Lintra and Linter, respectively.
4.1 Intra-class Constraint
Cintra aims enhance the representation capacity of M, which be designed to reduce the distance between M and corresponding positive sample. Commonly, multiple samples of one selected cluster
used in training batch and cluster commonly contains a limited number of samples, e.g., 16 samples of one cluster are sampled in training batch in previous works [53 22 and less than 30 samples in each cluster. Thus, Lentra can be designed to reduce the distance between M and corresponding positive training samples in each iteration. The intra-class constraint between M[c] and f; belonging to the c-th cluster can be formulated as:
Lintra (M(c), 2
[M[[] falla.
(6)
Its gradient w.r.t. M[c] is computed as: @CnntraaMi M[c] fc- aM(cc]
(7)

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- -I
AM
MMU
-211 AM
MU
MU
MU
Figure 2: Illustration of the advantage in inter-class constraint of the proposed objective function in Eqn. (8) w.r.t. different angles between M[i] and MMj]. Yellow arrows and blue arrows denote M and M[j, respectively. Green dashed arrows and red dashed arrows denote the updated MM by the proposed objective function Eqn. (8) and the previous method, respectively. It can be observed that the objective function in Eqn. { is more effective in pushing M[i away from M
Memory banks is encouraged approach positive samples by Amnta acquire the feature updates along with the update of A.
It is worth noting that setting Xintro 1 and Ainter makes Eqn. 5 equivalent to the momentum mechanism in Eqn. 2 This indicates that the previously used momentum mechanism is a special case of our BMW that only involves the intra-class constraint.
4.2 Inter-Class Constraint
Linter aims to enhance the discrimination capacity of MM. It can be intuitively designed to enlarge the distance between M and negative samples in D. However, only a small number clusters are selected in each training batch compared to the total number of clusters, e.g., only 16 clusters sampled in a single training batch previous works [53 22 and there are more 500 clusters D. Thus, training batch only contains limited information for the global inter-class constraint. We hence design Linter to enlarge the distance between different features inside M.
Previous methods for inter-class distance constraint commonly formulate the objective function as the negative value of Euclidean distance between two negative features as: - - M Miil
Its gradient w.r.t. M[i] is M[j] - M(... Then, the updated M[i with only inter-class constraint is
(1 + M.nter)MM enlarged by both enlarging the norm of M[ and the angle between M[i] and Mij]. As MMi is
Xinter-Mij]] whose norm larger than for Ainer E [0, 1 Thus, the distance
L2 normalized after rewriting, only the angle enlarging is retained and the distance enlarging via rewriting will be partially offset.
To chase an effective objective function for the inter-class constraint, we design Linter to pull M[i]
close to the opposite vector of M[j], which is formulated as:
Linter Einter(MM]] (MMiMMDD =
2 MM +M11E.
(8)
Its gradient w.r.t M[i] is computed as:
Linter (Mi)) Mii + MDJ. iM
(9)
For the iter-class constraint, rewriting with Eqn. 9 is more effective in enlarging the angle between Mii and M[jj, i.e., enlarging the distance between them after L2 normalization. Fig. shows the advantages of the proposed objective function for Cinter in Eqn. We also provide a theory proof of the advantage of Eqn. for one step update in Sec. A.I
4.3 Dynamically Weighting
Directly using the Euclidean distance in Eqn. and Eqn. is aware of the importance of each pair of samples. This paper proposes to dynamic weight these two constraints based on the idea that larger value of objective function corresponds to higher importance of each component. Hence,
II[(1 Ainter MMj] Ainter Minter M 1+ 2Amtter Ainter (1 - M[jMb] 1 MiiMb 1.
5

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weight Eqn. 6 and Eqn. by 1 H fill2 and HMMM M respectively. Then Lintra and Linter are re- formulatee as:
Lintra (Mic. - 8 [| - fillz- (M]] - ş [iMii + Mille. (10)
And their gradient w.r.t. M and M[i are computed (details are provided in A.2):
ft),
Mij.M(])) + MD)) (1I)
BMeel
BMN
This weighting strategy focuses on both hard positive pairs, i.e., the pair fi and M[c having small
cosine similarity, and hard negative pairs, f.e., the pair Mij] and Mia having large cosine similarity.
Eqn. generates larger gradients for hard pairs than easy ones, thus is aware of the pair importance.
5
Experiments
5.1 Dataset
Experiments are performed on Market-1501 [54] and MSMT17 [27] Market- 1501 contains 32,668
images of 1,501 identities captured from cameras Tsinghua University. 12,936 images of 751 identities are selected for training, and others are used for testing. In the test set, 3,368 images are selected query images and remaining 19,732 images are used gallery images. MSMT17 contains 126,441 images of 4,101 identities captured from 15 cameras at Peking University. 32,621 images of 1,041 identities are selected for training. and others are selected for testing. the test set, 11,659 images are selected as query images and remaining 82,161 images are used as gallery images. Due to the data usage restriction, experiments on DukeMTMC-relI) 5 are provided on the repository on github. Vehicle RelD task [5 is similar with the person RelD task, Limited by the paper length, experimental results on the vehicle dataset VeRi-776 [60] are provided in Sec. A..5 Following previous works 38
23 35
mAP, Rank1, Rank5 and Rank 10 accuracies are reported.
5.2 Implementation Detail
The proposed model adopts ResNet50 [6] pre-trained on ImageNet [62] as the feature extractor +. The last fully connected layer of ResNet50 is removed and the stride of the last residual block is set to 1. Following previous works [63 23 22 GEM pooling followed by batch normalization
layer [64] and L2-mormalization layer is added. Experiments with other backbones are provided in
Sec. A.3 Parameter in Eqn. ( is set to 0.05 following [22] Aintras and Ainter set as 0.9, and
0.02, res respectively. More details are provided in Sec. A.4
5.3
Model Analysis
In this section, we first analysis the effect of hyper-paramene Aintra Aintra and 7 on Market-1500 by varying the value of parameter and keeping others fixed the optimal value. We then evaluate the proposed inter-class constraint and the dynamically weighting strategy to show their validity. Finally, different sampling methods including hard, average, random and easy are evaluated.
5.3.1 Evaluation on hyper-parameters
Aintro affects the update rate for memory banks approaching positive training sam- ples, and thus affects the representation capacity of memory banks. The result of evaluation on Xintra is shown in Fig. 3 It can be observed that the performance in- sensitive to Ajotra in an appropriate rage from 0.8 0.9. And the best performance achieved when setting Xintra to 0.9. A larger value of Xintra gives more empha- sis to the intra-class constraint and makes Linter neglected by L.. While, a small value of Xintra releases the constraint on the representation capacity of memory banks and limits the compactness of intra-class samples.
mAP
86.5
84.5
82.5
0.5
0.6
0.7
0.8
0.9
1
Aintro
Figure Evaluation on Xintra-

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Ainter affects the update rate for push- ing memory banks away from each other,
mAP
87
and thus affects the discrimination capacity of memory banks. The result of evalua- tion on Ainter is shown in Fig. It can be observed that the best performance is achieved when setting Ainter 0.2.
78
0.05
0.1
0.15
0.2
0.25
0.3
interr
larger value of Ainter gives more empha- sis to the inter-class constraint and makes Lintra neglected by LM- While, a small value of Ainter releases the constraint on the discrimination capacity of memory banks and thus limits the separability of inter-class samples. Compared with the value of Aintra Ainter is smaller to achieve the best performance. This is because memory banks are initialized by the centroids of each clusters, thus are already close to positive samples. A larger value of Antra is needed to adapt to the feature update of training samples along with the update of 0. The balance between Xntra and Annrr is discussed in Sec. A.6
Figure 4: Evaluation on Xinter
affects the computation of Lg by adjust- ing the distribution of similarities between training samples and memory banks. The result of evaluation on is shown in Ta- ble [ It clear that setting T to 0.05 achieves the best performance.
Table 1: Evaluation
T
0.01
0.03
0.05
0.07
0.1
mAP(%)
84.5
85.6
86.3
82.5
79.1
5.3.2 Ablation study
Results of the ablation study on the pro-
Table Ablation study of BMW.
posed inter-class constraint and the pro-
posed dynamically weighting strategy is
Method
mAP
Rank
Rank5
Rank
summarized in T "baseline [22)" de-
baseline
79.7
90.5
96.2
97.3
notes the baseline method following the
w/o Canter
91.9
96.6
97.7
same settings [22], and neither the inter- class constraint or dynamically weighting strategy used in the baseline. "w/o finter" denotes only the intra-class con- straint and the dynamically weighting strat- egy are used. "|[M[i Mbbll in Eqn. 8 denotes using the Euclidean distance between two memory banks for the inter-constraint objective function as in previous methods.
[[ - Mblll Eqn.
83.1
92.5
98.0
w/o dynamically weighting
85.0
93.5
97.3
98.3
BMW
86.3
94.2
97.7
98.4
It can be observed from Table 2 that the proposed inter-class constraint effectively boosts the performance by large margin, e.g., improving the mAP from 81.5% 86.3%. It worth noting that the improvement of Linter is based on a strong baseline that achieves 81 81.5% mAP. This indicates that the separability of memory banks is important for discriminative Rell) feature learning, which neglected in previous methods. This also demonstrates that the proposed inter-class constraint is able to effectively enhance the Rell) feature optimization by completing the constraints of the objective function for the memory bank rewriting.
The proposed novel formulation in Eqn. ggnn.ffor for the inter-class constraint also enhances the performance as shown in Table 2 For example, it improves the mAP from 83.1% to 86.3% by 3.2% compared with the previous used ||M||] - Miill version. The comparison results clearly demonstrate that, with only one update step, the proposed formulation in Eqn. is more effective in enlarging the angle between two L2 normalized features memory banks.
As shown in Table the proposed dynamically weighting strategy further boosts the accuracy of the Rell) model from rather high performance, e.g., it boosts the mAP accuracy from 85.0% by 1.3%. Note that, compared to "baseline [22]". "w/o Linter" only additionally uses the proposed dynamically weighting strategy and achieves better performance, egg. 81.5% v.s. 79.7%. This demonstrates the generalizability of the proposed dynamically weighting strategy that consistently enhances the memory bank rewriting for both inter-class and intra-class constraints.
7

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Table 3: Comparison with SOTA methods on Market-1550 and MSMT17. "S. D." denotes the used labeled source data. " denotes multi-scale features are used. "f" denotes extra camera label is used.
Method
Backhone
Market-1500 Rank
MSMT-17
RankIO
MAP
UTAL
ResNer50
69.2
SpC.1
ResNe150
73.1
88.1
95.1
97.0
GCMT x7
ResNe:50
73.9
89.7
96.5
97.6
23.7
$4.3
11]
ResNer50
58.3
80.0
90.0
92.4
Market
13.2
31.6
RosNer50
77.7
92.7
PDA-Net
ResNet50
Duke
47.6
75,2
86.3
90.2
DIM-GLO
16
ResNe150
Dake
65.1
$8.3
94.7
96.3
Market- 1501
20.7
49.7
66.1
UDA 36
ResNe:50
Dake
53.7
75.8
$9.5
93.2
ResNet5O-IBN
MEB--eet 93
21
89.9
9650
97.5
MMT 35
ResNe:50
71.2
87.7
94.9
96.9
Duke
29.7
58.8
71.1
76.1
UDS M
78.0
91.2
96.2
97.7
35.1
64.4
76.2
80.5
92.8
97.1
73.4
77.8
PP --
RosNer50
CaCL X
ResNe150
MSMT17
84.7
93.8
97.7
98.1
Market-150 150]
36.5
66.6
75.3
80.1
DDC 33
ResYcL50-IBN
58.8
94.3
97.5
98.5
36.6
64.9
74.9
78.5
ICCS
ResNe150
72.1
88.8
95.3
96.9
18.6
45.7
57.5
62.8
D2CAP-FueDSI tv
ResNe150
83.4
93.3
97.2
98.3
44.2
73.7
83.6
86.7
ISE 63
ResNe:50
84.7
94.0
97.8
98.8
35.0
64.7
75.5
79.4
BMW
ResNe:50
86.3
94.2
97.7
98.4
44.6
75.5
86.3
87.1
To further illustrate the effectiveness of each com- ponent in this paper, we compare the training pro- cedures of the ablation study in Fig. [ It can be observed that different compared methods perform similarly in the start stage of the training. When models achieve a rather high performance, different compared methods show different abilities to further boost the performance. method without the intra-class constraint performs the worst, which indicates that ensuring the separa- bility of memory banks via the proposed intra-class constraint enables the model to gain additional dis- crimination capacity for the RelD task. It can also be observed that, compared with the previously used Euclidean distance in the inter-class constraint, the proposed new formulation Eqn. iSis more effective for memory bank rewriting within step.
mAP vs. Epoch
70-
25-
84-
can be observed that the
82
80
WED mden-clas constrant
Eudd doan Dustance Son.8
whth synsmica weighting
10
15
20
30
40
Ezech
Figure 5: Comparison of training procedures.
We also evaluate different sampling strategies includ- ing random sampling, average sampling and easy sampling for computing Eqn. The training proce- dures w.r.t. different strategies shown in Fig. 6 It can observed that easy sampling that select easy sample for memory rewriting performs the worst, which is due to limited information gain is delivered in those easy samples. The random sampling strat- egy performs similar with the hard sampling strategy because it also selects hard samples times. It clear that the hard sampling strategy performs the best through the entire training procedure.
mA7 Epech
AayAnTall kg
sureiin
Cooch
Figure 6: Comparison of sampling strategics.
5.4 Comparison with SOTA Methods
The comparison results one both Market-1500 1501 and MSMT17 with SOTA methods are summarized in Table 3 It's clear that the proposed BMW outperforms existing SOTA methods by clear margin. For example, BMW outperforms the recent SOTA method CaCL. )2 by 1.6% and 2.4% mAP on Market- 1501 and MSMT17, respectively. It is worth noting that CaCL additionally uses labeled

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source data for the model training. It can also be observed that BMW outperforms other domain adaptive methods that use extra labeled data, e.g. DIM+GLO [8] and MMT [38 by a large margin. This indicates that BMW more effective for discrminative feature optimization for the ReID task.
Compared with recent methods that use the momentum mechanism to rewrite memory banks 22 22 18] the proposed BMW outperforms them by a clear margin. For instance, BMW outperforms the recent DCC by 7.5% and 8.0% in mAP on Market- 1501 and MSMT17, respectively. This indicates that the inter-class constraint introduced in our BMW can effectively enhance the discrimination capacity of memory banks and therefore boosts the performance of learned features. It can also be observed that, even several methods use extra information, stronger backhone, multi-scale features to boost the performance, the proposed BMW still outperforms them by a clear margin. This further demonstrates the effectiveness of our method.
Conclusion
This paper proposes Bidirectionally Memory bank reWriting (BMW) method for the unsupervised person ReID task. The proposed BMW method addresses the limitations of existing memory bank rewriting algorithms that focus solely intra-class compactness while neglecting inter-class separability. By formulating the rewriting process as gradient descent update with dual objectives, i.e,, reducing intra-class diversity and enhancing inter-class separability, BMW effectively optimizes the representation and discrimination capacities of memory banks, thus provides an effective feature optimization for ReID model training. The newly designed objective formulation for the inter-class constraint further improves the separability of memory banks within a limited number of rewriting steps. Extensive experiments
standard benchmarks demonstrate the superior performance of BMW, even outperforming previous methods with stronger backbones. This simple yet effective approach establishes a new paradigm for unsupervised person ReID methods based on memory banks and has the potential to significantly advance the field of unsupervised person ReID.
Limitations and future research
While BMW shows promising performance, inter-class and inter-class constraints are combined with loss weights that need to be adjusted manually. As datasets could be biased, loss weights and the sampling strategy could be susceptible to replicate these biases. Therefore, for future improvement, 1) enhancing the dynamically weighting strategy to be aware of the distribution of different datasets, 2) a progressively sampling strategy w.r.t different stages of the model training, and 3) a distribution metric to guide the parameter settings, could be studied to enhance the robustness and effectiveness of the memory bank rewriting, thereby improving the performance of the trained ReID model.
Broader Impact
This paper innovatively formulates the memory bank rewriting as gradient decent update for objective functions, which may inspire new algorithms, theoretical, and experimental investigation. The proposed BMW method offers significant improvements on the unsupervised person ReID task and can also be used for many different vision tasks.
Acknowledgement
This work is supported in part by the National Key Research and Development Program of China under Grant 2024YFBB470800, in part by the National Natural Science Foundation of China under Grants U21A20486, 62473208, 92367105 and 62401294, in part by the Tianjin Science Fund for Distinguished Young Scholars under Grant 20JCIQJC00140, in part by the Postdoctoral Fellowship Program of CPSF under Grant Number GZC20240753, in part by the China Postdoctoral Science Foundation under Grant Number 2025M771529, in part by the China Postdoctoral Science Foundation Tianjin Joint Support Program under Grant Number 2025TO20TI, and in part by the Fundamental Research Funds for the Central Universities under Grant Number 63243155 and 63253238. Dr. Jianing Li is supported by P0048887 ITTP- ITS/028/22FP). PO051906 (RGC Early Career Scheme, 25600624), and P0054482 (Two Square Capital Limited donation).
Finally, Xiaobin Liu and Jianing Li would like to thank Xixi Wang and Yuning Zhou, respectively, for their invaluable support over the years.

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References
[1] Jialong Zuo. Ying Nie, Hanyu Zhou, Huaxin Zhang, Haoyu Wang, Tinyu Guo, Nong Sang, Changxin Gao. Cross-video identity correlating for person re-identification pre-training Advances in Neural Information Processing Systems, 37:25228-2553 252250, 2024.
[2] Geon Lec, Sanghoon Lec, Dohyung Kim, Younghoon Shin, Yongsang Yoon, and Bumsub Ham. Camera- driven representation learning for unsupervised domain adaptive person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1453-11 1462, 2023.
[3] Jianing Li, Shiliang Zhang, and Tiejun Huang. Multi-scale 3d convolution network for video based person re-identification. Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 8618-8625, 2019.
[4] Takashi Isobe, Dong Li, Lu Tian, Weihua Chen, Yi Shan, and Shengjin Wang. Towards discriminative representation learning for unsupervised person re- identification. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8526 8536, 2021.
[5] Chi Su, Jianing Li, Shiliang Zhang. Junliang Xing, Wen Gao, and Qi Tian. Pose-driven deep convolutional model for person re-identification. Proceedings of the IEEE international conference on computer vision, pages 1960-3969, 2017.
[6] Jianing Li, Shiliang Zhang, and Tiejun Huang. Multi-scale temporal cues learning for video person re-identification. IEEF Transactions on Image Processing, 29:4461-4473, 2020.
[7] Zhangyi Hu, Bin Yang, and Mang Ye. Empowering visible-infrared person re-identification with large foundation models. Advances in Neural Information Processing Systems, 11P:117333-117333 2024.
[8] Asmat Zahra, Nazia Perwaiz, Muhammad Shahzad, and Muhammad Moazam Fraz. Person re-identification: A retrospective domain specific open challenges and future trends. Pattern Recognition, 142: 109669, 2023.
[9] Jianing Li, Shiliang Zhang, Qi Tian, Meng Wang, and Wen Gao. Pose-guided representation learning for person re-identification. IEEE transactions on pattern analysis and machine intelligence, 44(2):622-635, 2019.
[10] Jiali Xi, Jianqiang Huang, Shibao Zheng. Qin Zhou, Bernt Schiele, Xian-Sheng Hua, Qianru Sun. Learning comprehensive global features in person re- re-identification: Ensuring discriminativeness of more local regions. Pattern Recognition, 134: 109068, 2023.
[11] Jianing Li, Jingdong Wang, Qi Tian, Wen Gao, and Shiliang Zhang. Global-local temporal representations for video person re-identification. In Proceedings of the IEEE/CVF international conference computer vision, pages 3958-3967, 2019.
[12] Xiaobin Liu, Shiliang Zhang, and Ming Yang. Self-guided hash coding for large-scale person re- identification. MIPR, 2019.
[13] Weihua Chen, Xiaotang Chen, Jianguo Zhang, and Kaiqi Huang. Beyond triplet loss: A deep quadruplet network for person re- re-identification. In CVPR, 2017.
[14] Jianing Li, Yaowei Wang, and Shiliang Zhang. Polarpose: Single-stage multi celti-pprron pose estimation in polar coordinates. IEEE Transactions on Image Processing, 32:1108 1119, 2023.
[15] Guodong Ding, Salman Khan, and Zhenmin Tang. Dispersion based clustering for unsupervised person re-identification. In BMVC, 2019.
[16] Yutian Lin, Xuanyi Dong, Liang Zheng, Yan Yan, and Yi Yang. A bottom-up clustering approach to unsupervised person re-identification. AAAI, 2019.
[17] Yixiao Ge, Dapeng Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Self-paced contrastive learning with hybrid memory for domain adaptive object re-id. In NeurIPS, 2020.
[18] Xiaobin Liu and Shiliang Zhang. Domain adaptive person re- re-ientification via coupling optimization. In ACM MM, 2020.
[19] Yanbei Chen, Xiatian Zhu, and Shaogang Gong. Instance-guided context rendering for cross-domain person re-identification. In /CCV, 2019.
[20] Yu-Jhe Li, Ci-Siang Lin, Yan-Bo Bo Lin, and Yu fu-Chiang Frank Wang. fross-dttaset person re- re-identification via unsupervised pose disentanglement and adaptation. In ICCV, 2019.
10

---

[21] Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li, and Yi Yang. Invariance matters: Exemplar memory for domain adaptive person re-identification. CVPR, 2019.
[22] Zuozhuo Dai, Guangyuan Wang. Weihao Yuan, Siyu Zhu, and Ping Tan. Cluster contrast for unsupervised person re-identification. In Proceedings of the Asian conference on computer vision, pages 1142-1160, 2022.
[23] Hantao Yao and Changsheng Xu. Dual cluster contrastive learning for object re-identification. arXiv preprint arXiv:21 2021.
[24] Chang Zou, Zeqi Chen, Zhichao Cui, Yuchu Liu, and Chi Zhang. Discrepant and multi-instance proxies for unsupervised person re-identification. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11058-1 10068, 2023.
[25] Zhun Zhong, Liang Zheng, Zhiming Luo, Shaozi Li, and Yang. Learning to adapt invariance in memory for person re-identification. IEEE Transactions on Puttern Analysis and Machine Intelligence, 2020.
[26] Jianing Li and Shiliang Zhang. Joint visual and temporal consistency for unsupervised domain adaptive person re- re-identification. In European conference on computer vision, pages 483 499.499..00 Springer, 2020.
[27] Longhui Wei. Shiliang Zhang, Wen Gao, and Qi Tian. Person transfer gan to bridge domain gap for person re-identification. In CVPR, 2018.
[28] Zhun Zhong, Liang Zheng, Shaozi Li, and Yi Yang. Generalizing a person retrieval model hetero-and homogencously. In ECCV, 2018.
[29] Yan Huang, Qiang Wu, JingSong Xu, and Zhong. Sbsgan: Suppression of inter-domain background shift for person re-identification. In ICCV, 2019.
[30] Jiawei Liu, Zheng-Jun Zha, Di Chen, Richang Hong, and Meng Wang. Adaptive transfer network for cross-domain person re-identification. In CVPR, 2019.
[31] Xiaobin Liu and Shiliang Zhang. Who closer: computational method for domain gap evaluation. Pattern Recognition, 122:108295; 2022.
[32] Yangru Huang, Peixi Peng, Yi Jin, Junliang Xing, Congyan Lang, and Songhe Feng. Domain adaptive attention model for unsupervised cross-domain person re-identification. arXiv preprint arXiv: 10529, 2019.
[33] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversaria training of neural networks. The Journal of Machine Learning Research, 17(1):096-20300 2016.
[34] Hlong-Xing Yu, Wei-Shi Zheng, Ancong Wu, Xiaowei Guo, Shaogang Gong, and Jian- Huang Lai. Unsu- pervised person re-identification by soft multilabel learning. In CVPR, 2019.
[35] Yiyu Chen, Zheyi Fan, Zhaoru Chen, and Yixuan Zhu. Ca-jaccard: camera-aware jaccard distance for person re-identification. In Proceedings of the IEEF/CVF conference on computer vision and pattern recognition, pages 17532-17541 17541, 2024.
[36] Liangchen Song, Cheng Wang, Lefei Zhang, Bo Du, Qian Zhang, Chang Huang, and Xinggang Wang. Unsupervised domain adaptive re-identification: Theory and practice. Puttern Recognition, 102:107173, 2020.
[37] Xiaobin Liu and Shiliang Zhang. Graph consistency based mean-teaching for unsupervised domain adaptive person re-identification. In Proceedingy of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, 2021.
[38] Yixiao Ge, Dapeng Chen, and Hongsheng Mutual mean beaa-teechhing Pseudo label refinery for unsuper- vised domain adaptation on person re-identification. In ICLR, 2020.
[39] Yunpeng Zhai. Qixiang Ye. Shijian Lu, Mengxi Jia, and Rongrong Ji. Multiple expert brainstorming for domain adaptive person re-identification. In ECCV, 2020.
[40] Lei Qi. Lei Wang, Jing Huo, Luping Zhou, Yinghuan Shi, and Yang Gao. novel unsupervised camera- aware domain adaptation framework for person re-identification. In /CCV, 2019.
[41] Minxian Li, Xiatian Zhu, and Shaogang Gong. Unsupervised tracklet person re- re-identification. IEEE T- T-PAMI, 2019.
11

---

[42] Minxian Li, Xiatian Zhu, and Shaogang Gong. Unsupervised person re-identification by deep learning tracklet association. ECCV, 2018.
[43] Yang Fu, Yunchao Wei, Guanshuo Wang, Yuqian Zhou, Honghui Shi, and Thomas S. Huang. Sclf-similarity grouping: A simple unsupervised cross domain adaptation approach for person re- re-identification. In ICCV, 2019.
[44] Xinyu Zhang. Jiewei Cao, Chunbua Shen, and Mingyu You. Self-training with progressive augmentation for unsupervised cross-domain person re-identification. In ICCV, 2019.
[45] Yoonki Cho, Woo Jae Kim, Seunghoon Hong, and Sung-Eui Yoon. Part-based pseudo label refinement for unsupervised person re-identification. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7308-7318, 2022.
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference computer vision and paitern recognition, pages 9729-9738. 2020.
[46]
[47] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non- parametric instance discrimination. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3733-3742, 2018.
[48] Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash. Mean shift for self-supervised learning. In Proceedings of the IEEF/CVF International Conference Computer Vision, pages 10326-10335, 2021.
[49] Yuhang Wu, Tengteng Huang, Haotian Yao, Chi Zhang, Yuanjic Shao, Chuchu Han, Changxin Gao, and Nong Sang. Multi-centroid representation network domain adaptive person re-id, Proceedings of the AAAI conference on artificial intelligence, pages 2750-2758, 2022.
[50] Jinjia Peng. Jiazuo Yu, Chengjun Wang, Huibing Wang, and Xianping Fu. Adapt only once: Fast unsupervised person re-identification via relevance-aware guidance. Pattern Recognition, 150: 10360, 2024.
[51] Menglin Wang, Baisheng Lai, Jianqiang Huang, Xiaojin Gong, and Xian-Sheng Hua. Camera-aware proxies for unsupervised person re-identification. In Proceedings the AAAI conference on artificial intelligence, pages 22724,227722222 2021.
[52] Dongkai Wang and Shiliang Zhang. Unsupervised person re- re-identification via multi- classification. In CVPR, 2020.
[53] Bo Han, Quamming Yao, Xingrui Yu, Gang Niu, Miao Xu. Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In NeurlPS, 2018
[54] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: benchmark. In ICCV, 2015.
[55] Zhedong Zheng, Liang Zheng, and Yi Yang. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In ICCV, 2017.
[56] Xiaobin Liu, Shiliang Zhang, Qingming Huang, and Wen Gao. Ram: region deep model for vehicle re-identification. In ICME, 2018.
[57] Xiaobin Liu, Shiliang Zhang. Xiaoyu Wang, Richang Hong, and Qi Tian. Group-group loss-based global- regional feature learning for vehicle re-identification. IEEE Transactions on Image Processing, 29:2638-266 2019.
[58] Shangzhi Teng, Xiaobin Liu, Shiliang Zhang, and Qingming Huang. Scan: Spatial and channel attention network for vehicle re-identiifcation. In PCM, 2018
[59] Longhui Wei, Xiaobin I. Liu, Jianing Li, and Shiliang Zhang, Vp-reid: Vehicle and person re-identification system. In ICMR, 2018.
[60] Xinchen Liu, Wu Liu, Huadong Ma, and Huiyuan Fu. Large-scale vehicle re-identification urban surveillance vidcos. In 2016 IEEE international conference on mudtimedia and expo (ICME), pages 1-6. IEEE, 2016.
[61] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016.
12

---

[62] J. Deng. W. Dong, Sacher, I..JJ Lá, Li, and I.. Fei-Fei. Imagenet: large-scale hierarchical image database. CVPR, 2009.
[63] Hao Luo, Youzhi Gu, Xingyu Liao, Shengi Lai, and Wei Jiang. Bag of tricks and a strong bascline for deep person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 0 2019.
[64] Sergey loffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. International conference on machine learning, pages 448-456, pmlr. 2015.
[65] Shiyu Xuan and Shiliang Zhang. Intra-inter domain similarity for unsupervised person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(3):1111-1726, 2022.
[66] Shiyu Xuan and Shiliang Zhang, Intra-inter camera similarity for unsupervised person re- re-idetiffictionn In Proceedings of the IEEF/CVF conference computer vision and paftern recognition, pages 11926-11935, 2021.
[67] Qingsong Hu, Huafeng Zhanxuan Hu, Feiping Nie. Diverse semantic information fusion for unsupervised person re-identification. Information Fusion, 107:102319, 2024.
[68] Xinyu Zhang, Dongdong Li, Zhigang Wang. Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, and Jingdong Wang. Implicit sample extension for unsupervised person re-identification. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 7369-7378 2022.
[69] Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random crasing data augmentation. AAAI, 2020.
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A
Appendix
A.1 Proof of the advantage of Eqn. g
AY
Ay
S
Ax
S
Sn1
span .... 0,, 0}}
Figure 7: Plot of AY, Ar and Ay in span [1, 0, 0, 0,...,
Theorem 1. Let E TR" be 1.2 normalized, ie.., | llvl 1. Consider 0 < X < and the following
X)æ xy, (update by previous methods)
b= 61 A)x Ay-(update by Eqn. X
(12)
Let (g.a) and 0 (g.b) be respectively the angle between and and that between and b. Then, ((y.b) 2 ((y.a)-
Proof. We only need consider the where y # x. Let X be the space expanded the be the projection of y on and Iu = y proj(y, X) * Since æ and yu yu/lly. || are orthogonal and normalized, there exists an orthonormal basis expanded by these two vectors. Therefore, there exists one isometry A such that Ax [1, 0, O..,. 0J R² and A(y1//ll /lyy II) [0, 1, O..., 0J E R.. Recall that isometry pre- serves norms and angles. the following, focues on (6)...6). ((Ay.Aa) and show that ((Ag.As) > (().
æ, proj(y, X)
Since y proj(y. X) + Vi and max(I1y1l) 11, |roj(y, X)II) < 1, there exist -1 < a < and 0 < B < such that a² + 80==== =1
Ay (a., 3, .....
By restricting on the subspace span ([1,0, 0, O,...... 0]. [0, 1, .... 0]} and letting Y span plot AY, Az and Ay Figure 7 We also characterize the three sets Sy. Str. SuI.. which have boundaries defined by AY, Ar and Ay as shown in Figure
St (uAr + wAy: > 0}
S (μAx vAy n1 0}
SmI (v E Rd (v, [3, -a, 0))
One verify that Aa and Ab both the set STz. Therefore, by denoting O, Asn.Ab) and 0g Alda,Ao)- we have < 0,, 02 <
O(Av.AD) = OcAvnde) + 06.
((Av.Aa) O(Avids) + O..
the following, we only need to show that 0, > 6a. have,
X tan 0, 1 - Aa tan 0. 1 Ao
A
Since 1+ Ao > 0, we consider two cases. For the first case, i.e. if 1f1-X A Aa <= 0, then 0, > 0., because tan 0, < (i.e.. & > π 7/2) or tan 6, is not well defined (i.e., 6, = x a/2) while tan 6. > 0 (i.c., 6, < x/2).
For the second case, ie., if 1 A Aa 0, then O, Oa because tan tan 0, > 0.
20

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A.2 Details of Gradient in Eqn. 11
OLintro (M
Míc -
x 4 x Di - S:l2
aMlc
[M(d - fill2
[MM -lM - A.)
2 (IIM(II1 11V12 - 2(M(D) SOMM AMMM) - 1t)
2 [1++1 +1 2(M())) (MMM)) R
(1 (Mee AIMMN- f.).
aLinter (Mil) (M[1) Mb1)
ME MMj
4 MM + +bI1
iM
|M + MM5l2
MM + + MDI)
2 (MIII IIMDI + 2(M)) MDD(MM + MbD)
2 +1+1+++200 2(M MDD(MN) MDD)
(1
+MD)-
A.3 Experiments with different backbones
Currently, ResNet50 is the most widely used backhone for unsupervised person ReID. Therefore, we adopt it in our paper ensuring fair comparisons with other approaches. To further validate our method's generality. we conduct additional experiments with ResNet18, ResNet34, and ViT-B/16. Key results on Market-1501 are summarized in Table 4 results on DukeMTMM-reID provided on the repository github.
Table 4: Performance comparison on Market- 1501 with different backhones.
Backbone
Method
Linter
ResNet18
BMW
67.9
w/o Linter
65.4
ResNet34
BMW
71.1
w/o Linter 84.6
ViT-B/16
BMW 87.6
The mAPs yielded with R and ResNet34 are lower than those with ResNet50 due to the smaller structures of the former two networks. The performances with V are slightly better than that with ResNet50 because of its stronger capacity. We do not adjust hyper- perametasre with those backbones and only evaluate our core contribution, i.e., the inter-class constraint.
It's clear that, with different backbones, the proposed inter-class constraint can always boost the ReID perfor- mance. These experimental results show that our BMW method could generalize well with different backbone. This is because our method is proposed to update the memory bank with L2-normalized features and it is backbone-agnostic. Thus, our BMW be applied any backbones without modification.
The experimental results demonstrate that the proposed inter-class constraint consistently enhances RelD performance across all tested backbones, confirming the strong generalizability our BMW method. This adaptability stems from our backbone agnostic design: the memory bank is updated with L2-normalized features. Therefore, BMW seamlessly integrates with any backbone architecture without requiring modifications.
A.4 Implementation details
We use the unlabeled training set target domain to train model, use testing set of target domain to
evaluate the model. DBSCAN is used for the unsupervised clustering and the eps is set as 0.6 following (22) Input images are resized to 256x 128. We use random flipping. random cropping, and random erasing (69 for
data augmentation. The Adam optimizer is adopted for training. Learning rate is initialized 0.00035 and
21

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decayed by 0.1 every epochs. Model is totally trained for 75 epochs. Model trained server with RTX 4090 GPU, and 128G memory.
each training hatch, we randomly sample 16 identities, ie., clusters by DBSCAN. cach sampled identity, 16 images are randomly selected to compose the training batch, resulting 256 images. For inter-class constraint computation (Cinter), we update the 16 memory banks corresponding to those sampled 16 identities by their closest memory bank vector (hard sampling strategy) via Linter.
Compared with the baseline model, BMW method introduces two extra steps:
1. Finding the closest memory bank vector for memory banks. This costs less than Ims for the memory hanks with size of less than 10000 (only around 1000 our experiments on person Rell) datasets) with the Intel(R) Core(TM) 17-14700 CPU.
2. Computing update vector for the 16 memory banks by Linter- As shown in Eqn. computing the update vector for cach memory bank only introduces one vector dot dot- product, one vector addition, one vector scaling, and one scalar addition. This only needs 4096 multiplications and 2049 additions with 2048 feature dimensions in paper and also costs less than Ims with CPU (will be faster with GPU).
Therefore, our method only introduces negligible overhead compared with the baseline methad. We also test the training time overhead: training the baseline model costs 3h25m33s, whereas ours costs 3h26m53s (just an extra 80s over roughly 3.5h).
As method only updates the memory bank used in the training process and only the backbone is used extract image features in inference process, our method does affect the inference efficiency.
The dynamic weighting could capture the overall distribution because:
1. For the intra-class constraint, 16 samples are randomly sampled for cach cluster, which could be enough to provide approximate reflection of the entire sample distribution in this cluster. For instance, there roughly 20 samples in each cluster in the training process Market-1501.
2. For the inter-class constraint, the hard negative sample is selected from all memory banks of the dataset, i.e., it is class-wise. Thus, it can also sense the overall data distribution.
A.5 Experiments on VeRi-776
Ve6i-776 contains 37,746 images of 576 vehicles for training. 1,678 images and 51,003 images of another 200 vehicles are used as queries and galleries, respectively. The comparison results on VeRi-776 with other methods summarized in T
Table 5: Comparison with SOTA methods on VeRi-776.
Method
Reference
Source Data
mAP
MMT
38
ICLR 2020
VehicleID
35.3
SpCL
NeurIPS 2020
VehicleID
38.9
Cluster Contrast
22
ACCV 2022
40.3
Cluster Contrast (Infomap)
22
ACCV 2022
40.8
DCC
23
arxiv 2022
41.4
PPLR (part feature)
45
CVPR 2022
41.6
BMW
This paper
42.1
DCC (ResNet50-ibn)
233
arxiv 2022
42.1
PPLR PprR(part feature+cameea labels) 45
CVPR 2022
43.5
PPLR(part feature+camera labels+re-ranking) 45
CVPR 2022
45.3
It can be observed from Tabel our BMW method also outperforms other methods on the vehicle ReID task.
For example, PPLR (part features) uses extra part features and our BMW still outperforms it by 0.5% in mAP Our BMW method even comparable with methods that deeper backbone [DCC (e extra annotations [PPLR (part features camera labels)] or stronger re-ranking PPLR (part features + camera labels + re- re-ranking)..
As we use the same parammtarsssers on the VeRi-776 datasct, ie., Acntra 0.9 and Ainter 0.2, experiments on VeRi-776 further show that hyper-parameter selected on one dataset can be applied on other datasets with promising performance.
22

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A.6 Balance between the intra-class and inter-class constraints
The two constraints are balanced with two weights, i.e, Aintra the intra-class constraint Cintras and Ainter for the inter-class constraint Cinter. The values of Aintra and Vinter should be different due to their distinct roles:
The term Lintra encourages memory banks to approach corresponding positive training samples, a larger Aintra enhances the intra-class constraint and encourages the memory banks to closely represent their positive samples while maintaining discriminative power in feature space.
2. In the meantime, since Linter pushes memory banks away from each other, larger Vinter may push memory banks far away from corresponding positive samples.
Therefore, Ainter should be smaller than Aintra from the above analysis.
We also conduct the hyper-parameter analysis, which shows that:
1. The configuration (Aintra 0.9, Ainter 0.2) achieves peak performance on both datasets. This indicates that hyperparameters selected on one dataset can be applied to others without sacrificing the performance.
2. The superior performance does not totally rely on careful selection of hyperparameters. We notice that BMW consistently achieves better results than others as long as Xintra sits in [0.6, 1] and Ainter in [0.1, [0.1..225] 0.25).
Consequently, the hyperparameters are easily tunable for superior performance across different datasets.
By the way, the superiority range of Actter is smaller than that of Aintra This is because Cintra decreases after several training iterations even when using the hard sampling strategy, while Linter stays large because of the large number of identitics. We can apply threshold Linter ignore memory bank pairs that already far away from cach other. This strategy will enlarge the operating range of Ainter.
A.7 Limitations
A.7.1
Noisy data
Our method is applicd to the unsupervised person ReID task. As the pscudo label is generated by unsupervised clustering method, ie., DBSCAN, the training process is performed with noisy in the label. Along with the model training, the discrimination ability of the model is progressively enhanced. The pscudo label generated by DBSCAN based extracted features will also progressively more precise, which in turn helps the model training. However, the noisy label mislead both model training the memory bank optimization. Therefore, for future improvement, a robust memory bank optimization method should be studied that better captures relationships between memory entries and training samples to improve effectiveness.
A.7.2 Cross-domain scenarios
Our experiments demonstrate both strong performance and consistent hyperparameter transferability across datasets. While current unsupervised person RelD methods typically train and test on the same dataset, cross- domain evaluation reveals significant performance degradation due to distribution shifts.
Recent work in lifelong person ReID has begun addressing this challenge through memory bank-based knowledge preservation. Current approaches primarily optimize memory hanks using only positive sample relationships. We observe that incorporating inter-class constraints, knowledge representation. Building on this insight, future research should investigate BMW- memory bank optimization to improve performance in lifelong ReID scenarios. This direction could potentially bridge the gap between single-domain and cross-domain RelD performance.
our BMW framework, could significantly enhance
A.7.3 Optimization effectiveness
For datasets with a large number of identities, the inter-class loss may remain elevated during training. Therefore, we need to adjust inter-class constraints, For future research, dynamically weighting strategy and a progressively hard sampling strategy could be studied to enhance the robustness and effectiveness of our method.
A.8 Compared with the conventional classifier
It's an important topic about memory bank is better than FC layer for unsupervised person ReID, We provide the reasons as follows.
Memory bank is more effective in enhancing feature representation:
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1. RellD is different from classification: In the classification task, the training and test have the same set of categories. However, in RelD task, identities in the training set not exist the test set (because we cannot know identities appear in the future). So. a classifier trained on the training set can not predict correct ID labels on the test set. As RelD is performed based on image feature similarities. feature distance relationship more important than classification prediction.
2. Classifier is not effective to optimize features: The standard classifier uses an FC layer and SoftMax layer to predict probabilities. The cross-entropy loss is commonly used to supervise the predicted probabilities. Classifier focuses on predicting correct labels, instead optimizing feature distance relationships, as widely discussed in previous works.
Memory bank is more effective to optimize features: The memory bank is optimized separately with the features. The target of memory hank update enhance the feature distance optimization and performance of RelD, rather than accurately predicting image labels. E.g., although Lo increases from 0.439 3.033 in the epoch 1 on Market, mAP on test increases from 12.9% to 25.6%.
Memory bank more robust against label noise induced by unsupervised clustering:
1. A single noisy sample will perturb all parameters in FC layer: As an FC is followed by the SoftMax layer, cach noisy sample will perturb all FC parameters via the gradient of the cross entropy loss, making FC parameters sensitive to label noise.
2. Only few memory banks perturbed by single noisy sample: For noisy sample in training batch, only corresponding memory bank is certainly to be perturbed. Other memory banks perturbed only if noisy label selected as hard sample, which a very low probability of occurrence given the large number of identities in datasets. Thus, most memory banks will not be perturbed by single noisy sample.
Therefore, memory banks are better for unsupervised ReID with stronger generalization eapacity on test
On the supervised ReID task, memory bank can also enhance feature representations more effectively compared to conventional classifiers. Below, we provide the detailed explanation and validation.
BMW more effective feature optimization:
1. Different from classification tasks, ReID requires learning discriminative feature representations where feature distance relationships (e.g., intra-class compactness and inter-class separation) are optimized, rather than classification accuracy.
2. As conventional classifiers focus accurate label prediction and do not directly optimize feature relationships, their abilities optimize feature representations are limited.
3. Compared conventional classifiers, the proposed BMW directly optimizes feature distance rela- tionships with Co, and memory banks optimized with L.M facilitate the feature representation lcarning.
From the above analysis. the advantage of our BMW in feature representation learning over conventional classi- fiers is independent of data annotations. Thus, BMW also effective optimizing feature representations on the supervised ReID task compared to conventional classifiers.
We further trained our BMW method under the supervised setting. The performance comparison between our BMW and conventional classifiers is summarized Table
Table 6: Comparison among classifier-based and memory- bank-based methods.
Method
Type
Reference
mAP Market
mAP MSMT17
Only classifier Classifier+cirele loss
Classifier
JJCA121
81.1
Classifier
CVPR'20
87.4
52.1
ICE ISE
Memory bank
ICCV'21
86.6
50.4
Memory bank
CVPR'22
87.8
51.0
DCMIP
Memory bank
ICCV'23
89.2
62.8
DCC
Memory bank
arXiv'22
89.9
65.5
Unsupervised BMW
Memory bank
This paper
86.3
44.6
Supervised BMW
Memory bank
This paper
90.5
66.7
It can be observed from Table that:

---

1. Compared to the conventional classifier, our BMW still enhances feature representations effec- rively under the supervised setting.
2. Our unsupervised version of BMW is even comparable to the supervised circle loss method, which is metric learning method. This shows the stronger feature optimization capacity of our BMW method.
3. Our BMW also outperforms other methods using memory bank by a clear margin. This further shows the superiority of our BMW method.
25


Reviews:
[{"review_id": "Cq4trSfX2d", "reviewer": "Reviewer_a7FY", "summary": "This paper proposes a new memory bank rewriting mechanism called bidirectional memory bank rewriting (BMW), which represents memory bank rewriting as a gradient descent update with two objectives to improve category recognition and enhance feature discrimination capabilities. Several techniques are used to effectively improve memory bank representation capabilities for the two objectives and optimization, thereby achieving ReID feature optimization.", "questions": "1.This paper lacks validation across multiple datasets and different tasks, making it difficult to fully demonstrate the model's superiority.  \n\n2.The paper contains numerous textual errors and formatting issues.  \n\n3.The dynamic weighting scheme lacks detailed description, making the derivation process of Equations 10 to 11 difficult to understand.  \n\n4.The paper's charts and graphs are unclear, affecting readability and information comprehension.", "limitations": "yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\n1.This paper proposes a bidirectional objective gradient descent update method for memory bank rewriting, which aims to improve feature discrimination capabilities. \n\n2.For two-objective updates, this paper proposes an L2 norm and dynamic weighting strategy, which effectively improves memory bank representation capabilities and achieves ReID feature optimization.\n\nWeaknesses:\n\n1.The model was not evaluated on the VeRi-776 and DukeMTMC-reID datasets, limiting the ability to validate its overall superiority. Furthermore, the results in Table 1 indicate suboptimal performance on the MSMT-17 dataset.\n\n2.Several minor errors are present in the manuscript, including incorrect labeling of variables i and j in Equations (8) and (10).\n\n3.Figures 1 and 2 lack clarity, making it difficult for readers to understand the presented concepts.\n\n4.The data format in Table 1 is inconsistent, mixing percentages and decimal values. Additionally, the proposed method is highly sensitive to hyperparameters and lacks cross-dataset hyperparameter analysis, limiting its applicability to real-world recognition tasks.\n\n5.The introduction lacks analytical rigor, the literature review is insufficient, and the inter-class constrained memory bank approach is not original to this work. Moreover, although domain adaptation for pedestrian re-identification is mentioned, the experimental section does not include relevant analysis.", "was_revised": true, "final_justification": "The authors' clarification helps to resolve the main issues I previously raised.", "rebuttal": "**Response to the Reviewer a7FY**\n\n---\nThank you very much for your time and insightful comments. We sincerely appreciate your recognition of our core contribution. Point-by-point responses and revision plans to each concern are given as follow:\n\n---\n**Q1:** Validation on DukeMTMC-reID and VeRi-776, performance on MSMT-17 in Table 1 (actually it's Table 3 in our manuscript), and cross-dataset hyperparameter analysis.\n\n**A1:** We provide the following clarification for each dataset:\n\n- DukeMTMC-reID (Duke for short) and cross-dataset hyperparameter analysis:  \nActually, we have compared our methods on Duke with other approaches before the submission. However, Duke has been taken down while some previous conferences, e.g., ICCV 2025, explicitly prohibited using it. Due to the concerns of this restriction, we removed the results on Duke from the comparison with SOTAs in Table 3. We provide those results as:\n  - Comparison with recent SOTAs. Note that works after 2022 commonly don't use Duke.\n\n    |Method|Reference|Source Data|mAP|Rank1|Rank5|Rank10|\n    |-|-|:-:|:-:|:-:|:-:|:-:|\n    |MMT|ICLR'20|Market-1501|63.1|76.8|88.0|92.2|\n    |MEB-Net|ECCV'20|Market-1501|66.1|79.6|88.3|92.2|\n    |CCL+PDA+FA|ICCV'21|Market-1501|70.8|83.5|-|-|\n    |GCMT|IJCAI'21|-|63.6|78.2|88.6|91.3|\n    |GCMT|IJCAI'21|Market-1501|67.8|81.1|91.1|93.9|\n    |IICS|CVPR'21|-|64.4|80.0|89.0|91.6|\n    |P $^2$ LR|AAAI'22|Market-1501|70.8|82.6|90.8|93.7|\n    |IIDS|T-PAMI'24|-|68.7|82.1|90.8|93.7|\n    |NPSSL|T-ETCI'25|-|67.6|78.9|88.6|92.0|\n    |BMW|This paper|-|**75.2**|**86.0**|**92.6**|**95.0**|\n    ||\n\nThe above comparison on Duke shows that our BMW method also achieves the best performance. For instance, although P $^2$ LR \\[AAAI'22\\] uses extra training data, BMW still outperforms it by 4.2% in mAP. This further demonstrates the effectiveness of our BMW.\n\nWe further conduct the parameter analysis on Duke as follows:\n  - Evaluation on $\\lambda_{intra}$  \n    ||||||||\n    |-|-|-|-|-|-|-|\n    |$\\lambda_{intra}$|0.5|0.6|0.7|0.8|0.9|1|\n    |mAP on Duke|69.7|73.6|74.5|75.0|**75.2**|75.1|\n    ||\n  - Evaluation on $\\lambda_{inter}$  \n    |||||||||\n    |-|-|-|-|-|-|-|-|\n    |$\\lambda_{inter}$|0|0.05|0.1|0.15|0.2|0.25|0.3|\n    |mAP on Duke|67.8|70.6|72.5|74.4|**75.2**|74.1|65.5|\n    ||\n  - Evaluation on $\\tau$  \n    |||||||\n    |-|-|-|-|-|-|\n    |$\\tau$|0.01|0.03|0.05|0.07|0.1|\n    |mAP on Duke|74.4|75.0|**75.2**|73.5|72.1|\n    ||\n  - Ablation study of BMW\n\n    |||||||\n    |-|:-:|:-:|:-:|:-:|-|\n    |Method|baseline [15]|w/o $\\mathcal{L}_{inter}$|$\\|\\|\\mathcal{M}[i]-\\mathcal{M}[j]\\|\\|_2^2$ in Eqn.8|w/o dynamically weighting|BMW|\n    |mAP on Duke|66.5|67.8|71.6|73.3|**75.2**|\n    ||\n\nThe model analysis results on Duke show that the best performance is achieved when $\\lambda_{intra}=0.9$, $\\lambda_{inter}=0.2$, and $\\tau=0.05$, which are the optimal parameters for BMW on Market-1501. This indicates that hyperparameters selected on one dataset can be applied to others without sacrificing the performance. Meanwhile, the superior performance does not totally rely on careful selection of hyperparameters. We notice that BMW consistently achieves promising performance as long as $\\lambda_{intra}$ sits in $[0.6,1]$ and $\\lambda_{inter}$ in $[0.1,0.25]$.\n\nThe superiority range of $\\lambda_{inter}$ is smaller than that of $\\lambda _ {intra}$. This is because $\\mathcal{L} _ {intra}$ decreases after several training iterations even when using the hard sampling strategy, while $\\mathcal{L} _ {inter}$ stays large because of the large number of identities. We can apply a threshold on $\\mathcal{L} _ {inter}$ to ignore memory bank pairs that are already far away from each other. This strategy will enlarge the operating range of $\\lambda_{inter}$.\n\nThe temperature hyperparameter $\\tau$ is widely used in constrastive learning methods. It's commonly set to 0.05 in previous works and our BMW achieves promising performance when setting it in the range of \\[0.01,0.05\\].\n\nThe ablation study on Duke shows that proposed methods effectively boost the ReID performance on differnet datasets.\n\nDue to the data usage restriction, we plan to include these results on Duke only in our github repository, not in the paper.\n\n- VeRi:  \nThank your for pointing out that VeRi can be used for validation on the vehicle ReID task. Currently, we are still awaiting the authors' response regarding data access. Experiments will be performed once we get the data.\n\n- MSMT-17:  \nIn Table 3, the results on MSMT-17 of MEB-Net and MMT are mistakenly listed as their performance on Duke. Due to the last-minute removal of Duke results in Table 3, we mistakenly left these entries showing Duke results. We correct these values as follows. Note that, MEB-Net does not use MSMT-17, it only reports its performance on Matker-1501 and Duke.\n\n    |Method|Reference|Source Data|mAP|Rank1|Rank5|Rank10|\n    |:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n    |MMT|ICLR'20|Market-1501|26.6|54.4|67.6|72.9|\n    |MMT|ICLR'20|Duke|29.7|58.8|71.1|76.1|\n    |BMW|This paper|-|**44.6**|**75.5**|**86.3**|**87.1**|\n    ||\n\nFrom the corrected values, our BMW method also outperforms recent works by a clear margin on MSMT-17.\n\nDue to the large number of identities and the large variation in intra-ID images, MSMT-17 is more challanging than Market-1501 and Duke, thus current methods achieve lower performance on MSMT-17 than on Market-1501 and Duke.  \n\nWe will correct these entries in Table 3 and further clarify the comparison on MSMT-17 in the final version.  \n\n---\n**Q2:** Textual errors and formatting issues.\n\n**A2:** Thanks for pointing out that. We have carefully proofread and fixed these issues:\n\n- Equations (8) and (10): We correct variable labels as:  \nIn Equation (8): $\\mathcal{L} _ {inter}(\\mathcal{M}[i],\\mathcal{M}[j])=\\frac{1}{2}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\| _ 2^2.$  \nIn Equation (10): $\\mathcal{L}_{inter}(\\mathcal{M}[i],\\mathcal{M}[j])=\\frac{1}{8}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2^4.$\n\n- Data format in Table 3: We unify the data in Table 3 to percentage format with 1 decimal place.\n\nThese issues along with several typos will be corrected in the final version. We further group methods by their backbones and source data in Table 3 for easier comparison.\n\n---\n**Q3:** Detailed description of the dynamic weighting scheme.\n\n**A3:** Good suggestion! The dynamic weight scheme is based on the idea that larger value of objective function needs larger weight. Hence, the weights for Eqn.(6) and Eqn.(8) are $\\frac{1}{4}\\|\\mathcal{M}[c]-f_i\\| _ 2^2$ and $\\frac{1}{4}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\| _ 2^2$, respectively. Then, $\\mathcal{L} _ {intra}$ and $\\mathcal{L} _ {inter}$ are:  \n- $\\mathcal{L} _ {intra}(\\mathcal{M}[c],f _ i)=\\frac{1}{4}\\|\\mathcal{M}[c]-f _ i\\| _ 2^2\\times\\frac{1}{2}\\|\\mathcal{M}[c]-f_i\\| _ 2^2=\\frac{1}{8}\\|\\mathcal{M}[c]-f _ i\\| _ 2^4$,\n\n- $\\mathcal{L} _ {inter}(\\mathcal{M}[i],\\mathcal{M}[j])=\\frac{1}{4}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\| _ 2^2\\times\\frac{1}{2}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\| _ 2^2=\\frac{1}{8}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\| _ 2^4.$\n\nTheir gradient w.r.t. $\\mathcal{M}[c]$ and $\\mathcal{M}[i]$ are:\n\n- $\\frac{\\partial \\mathcal{L}_{intra}(\\mathcal{M}[c],f_i)}{\\partial\\mathcal{M}[c]}$  \n$=\\frac{1}{8}\\times4\\times\\|\\mathcal{M}[c]-f_i\\|_2^3\\times\\frac{\\mathcal{M}[c]-f_i}{\\|\\mathcal{M}[c]-f_i\\|_2}$  \n$=\\frac{1}{2}\\|\\mathcal{M}[c]-f_i\\|_2^2(\\mathcal{M}[c]-f_i)$  \n$=\\frac{1}{2}(\\|\\mathcal{M}[c]\\|_2^2+\\|f_i\\|_2^2-2(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$  \n$=\\frac{1}{2}(1+1-2(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$  \n$=(1-(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$,\n\n- $\\frac{\\partial \\mathcal{L}_{inter}(\\mathcal{M}[i],\\mathcal{M}[j])}{\\partial\\mathcal{M}[i]}$  \n$=\\frac{1}{8}\\times4\\times\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2^3\\times\\frac{\\mathcal{M}[i]+\\mathcal{M}[j]}{\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2}$  \n$=\\frac{1}{2}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2^2(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n$=\\frac{1}{2}(\\|\\mathcal{M}[i]\\|_2^2+\\|\\mathcal{M}[j]\\|_2^2+2(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n$=\\frac{1}{2}(1+1+2(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n$=(1+(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$.\n\nThese will be added in the revised version for an easier understanding.\n\n---\n**Q4:** Charts and graphs are unclear.\n\n**A4:** We will enhance the color contrast and add more textual notes for figures. Detailed captions for Figures 1 and 2 will be provided as follows:  \n \n  - \"Figure 1: Illustration of the memory bank update without (w/o) and with (w/) the inter-class constraint. Dots and stars denote features and memory banks. Different colors denote different clusters. Without the inter-class constraint, memory banks are pulled to approach positive samples only (shown as yellow arrows). With the inter-class constraint, memory banks are also push away from each other (shown as purple arrows), thus enhancing their separability.\"\n  - \"Figure 2: Illustration of the advantage in inter-class constraint of the proposed objective function in Eqn.(8) w.r.t. different angles between $\\mathcal{M}[i]$ and $\\mathcal{M}[j]$. Yellow arrows and blue arrows denote $\\mathcal{M}[i]$ and $\\mathcal{M}[j]$, respectively. Green dashed arrows and red dashed arrows denote the updated $\\mathcal{M}[i]$ by the proposed objective function in Eqn.(8) and the previous method, respectively. It can be observed that the objective function in Eqn.(8) is more effective in pushing $\\mathcal{M}[i]$ away from $\\mathcal{M}[j]$.\"\n\nThese will be added in the revised version for clear illustrations.\n\n**Q5:** More related works and analysis in experiments.\n\n**A5**: Thanks for the advise. The final version will include (1) more related works, e.g., FUReID, IIDS, etc. (2) Analysis with domain adaptation ReID in experiments.\n\n---\nWe believe these revisions will address the concerns and significantly strengthen our paper.\n\nThank you again for your invaluable feedback."}, {"review_id": "4ZaHwEG4WF", "reviewer": "Reviewer_RyMZ", "summary": "This paper proposes a novel method for memory bank update, which is a core procedure in unsupervised learning of person re-identification models. Different from previous works, the proposed method introduces an inter-class constraint in the memory bank update as well as an intra-class constraint. Also, a weighting strategy for controlling the trade-off between these two contrainsts boosts the performance. Comprehensive empirical studies demonstrate the effectiveness of the proposed methods.", "questions": "Please see the Weaknesses.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "[Strengths]\n- This paper is very well-written and easy to follow.\n- The motivation for the inter-class constraint in memory bank update is clear and the analysis on the memory bank update mechanism from the gradient descent perspective is intriguing, providing useful insights.\n- The proposed method is simple yet effective.\n- Comprehensive experiment results demonstrating the proposed method are provided.\n\n[Weaknesses]\n- The experiments are conducted only with a ResNet50 backbone. Results with other backbones such as ViT would help validate the generality of the method.\n- Some implementation details on how Equations (8) and (9) are implemented in practice (e.g. explanation with a mini-batch) are not clear. More clarification would be helpful.\n- Minor typos are found: (1) In Equations (8) and (10), the term M[i] appears twice in $L_{inter}$, (2) In Figure 2’s legend, the red text includes a duplicated M[j], (3) Some numerical values in Table 3 appear to require scaling (e.g., 0.383 should likely be 38.3).", "was_revised": true, "final_justification": "This work proposes a novel unsupervised person re-identification method with sufficient contributions. I recommend to accept this paper.", "rebuttal": "**Response to the Reviewer RyMZ**\n\n----\n\nWe sincerely appreciate your recognition of the paper's novelty, originality, and clarity. And we thank you very much for your time and insightful comments. The point-by-point detailed response and the revision plans to each concern are given as follows:\n\n---\n**Q1:** Experiments with other backbones.\n\n**A1:** Thank you for the valuable suggestion!\nCurrently, ResNet50 is the most widely used backbone for unsupervised person ReID. Therefore, we adopt it in our paper ensuring fair comparisons with other approaches. \nTo further validate our method’s generality, we conduct additional experiments with ResNet18, ResNet34, and ViT-B/16. Key results are summarized below:\n\n- ResNet18: \n   |Method|mAP on Market-1501|mAP on DukeMTMC-reID|\n   |:-:|:-:|:-:|\n   |w/o $\\mathcal{L} _ {inter}$|57.1|52.2|\n   |BMW|**67.9**|**58.0**|\n   ||\n\n- ResNet34: \n   |Method|mAP on Market-1501|mAP on DukeMTMC-reID|\n   |:-:|:-:|:-:|\n   |w/o $\\mathcal{L} _ {inter}$|65.4|60.8|\n   |BMW|**71.1**|**64.5**|\n   | |\n   |MMT[ICLR'20\\] with ResNet50|71.2|63.1|\n   |IICS\\[CVPR'21\\] with ResNet50|72.9|64.4|\n   ||\n\n- ViT-B/16 (The initial weights are pretrained on the ImageNet-21K and then finetuned on ImageNet-1K following TransReID\\[ICCV'21\\]. We do not use the jigsaw patch shuffling module or side information embedding that are proposed in TransReID\\[ICCV'21\\]): \n   |Method|mAP on Market-1501|mAP on DukeMTMC-reID|\n   |:-:|:-:|:-:|\n   |w/o $\\mathcal{L} _ {inter}$|84.6|73.5|\n   |BMW|**87.6**|**77.3**|\n   ||\n   \nThe mAPs yielded with ResNet18 and ResNet34 are lower than those with ResNet50 due to the smaller structures of the former two networks.\nThe performances with ViT-B/16 are slightly better than that with ResNet50 because of its stronger capacity. Due to the limited time for those additional experiments, we do not adjust hyper-parameters with those backbones and only evaluate our core contribution, *i.e.*, the inter-class constraint.\n\nIt's clear that, with different backbones, the proposed inter-class constraint can always boost the ReID performance.\nThese experimental results show that our BMW method could generalize well with different backbone.\nThis is because our method is proposed to update the memory bank with L2-normalized features and it is backbone-agnostic. Thus, our BMW can be applied to any backbones without modification.\n\nThe experimental results demonstrate that the proposed inter-class constraint consistently enhances ReID performance across all tested backbones, confirming the strong generalizability of our BMW method. This adaptability stems from our backbone-agnostic design: the memory bank is updated with L2-normalized features. Therefore, BMW seamlessly integrates with any backbone architecture without requiring modifications.\n\nNotably, our method achieves superior performance even with smaller backbones.  For instance, our BMW with ResNet34 attains 64.5 in mAP on DukeMTMC-reID, outperforming IICS\\[CVPR'21\\] and MMT\\[ICLR'20\\] that use ResNet50. This result further demonstrates the effectiveness of our approach in ReID model training.\n\nAdditionally, as detailed in our response to Reviewer a7FY, we have included supplementary experiments on DukeMTMC-reID using ResNet50. These results:\n\n- Show our method's clear performance advantage over competitors on DukeMTMC-reID.\n\n- Exhibit superior performance when applying the same hyperparameters across different datasets without additional tuning.\n\nWhile these DukeMTMC-reID results further validate our method's generalizability, data usage restrictions require us to include them only in our GitHub repository rather than the final paper.\n\n---\n**Q2:** Details for Equations (8) and (9).\n\n**A2:** Thanks for pointing out that.\nIn each training batch, we randomly sample 16 identities, *i.e.*, clusters by DBSCAN. For each sampled identity, 16 images are randomly selected.\nFor inter-class constraint computation ($\\mathcal{L} _ {inter}$), we update the 16 memory banks corresponding to those sampled 16 identities by their closest memory bank vector (hard sampling strategy) via $\\mathcal{L} _ {inter}$. \nThese details will be added in the revised version for a clearer description.\n\nBy the way, we also provide detailed descriptions about the derivation process in our response to the Reviewer a7FY. We hope those will help to clarify our method.\n\n\n---\n**Q3:** Textual errors and formatting issues.\n\n**A3:** We have carefully proofread the manuscript and fixed these issues:\n\n- Equations (8) and (10): We correct variable labels as:  \nIn Equation (8): $\\mathcal{L} _ {inter}(\\mathcal{M}[i],\\mathcal{M}[j])=\\frac{1}{2}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\| _ 2^2.$  \nIn Equation (10): $\\mathcal{L}_{inter}(\\mathcal{M}[i],\\mathcal{M}[j])=\\frac{1}{8}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2^4.$\n\n- We enhance the color contrast and correct the legend for Figure 2. We also update the caption of Figure 2 as follows:\n  \n     \"Figure 2: Illustration of the advantage in inter-class constraint of the proposed objective function in Eqn.(8) w.r.t. different angles between $\\mathcal{M}[i]$ and $\\mathcal{M}[j]$. Yellow arrows and blue arrows denote  $\\mathcal{M}[i]$ and $\\mathcal{M}[j]$, respectively. Green dashed arrows and red dashed arrows denote the updated $\\mathcal{M}[i]$ by the proposed objective function in Eqn.(8) and the previous method. It can be observed that the objective function in Eqn.(8) is more effective in pushing $\\mathcal{M}[i]$ away from $\\mathcal{M}[j]$.\"\n\n\n- Data format in Table 3: We unify the data in Table 3 to percentage format with 1 decimal place.\n\nThese issues along with several typos will be corrected in the final version.\n\n---\nWe believe these revisions will address your concerns and significantly strengthen our paper.\n\nThank you again for your recognition of our paper and invaluable feedback."}, {"review_id": "1ezbZotDDg", "reviewer": "Reviewer_p7UF", "summary": "The paper introduces a novel memory bank rewriting mechanism (BMW) aimed at enhancing the performance of unsupervised person re-identification (ReID). BMW leverages a bidirectional memory bank rewriting approach that integrates intra-class and inter-class constraints to improve the representation and discrimination capabilities of the memory bank. Specifically, BMW treats the memory bank rewriting process as a gradient descent update, optimizing both intra-class compactness and inter-class separability simultaneously. Experimental results demonstrate that BMW achieves excellent performance on standard benchmarks such as Market-1501 and MSMT17, even outperforming methods with stronger backbone networks, thus validating its effectiveness and potential in the field of unsupervised ReID.", "questions": "1. In Sec. 4, how do the authors ensure that the intra-class and inter-class constraints in BMW are balanced in practice? Are there any challenges in tuning hyperparameters?  \n\n2. The experimental section does not address BMW's computational complexity and model efficiency, which are critical for real-world applications. Can the authors provide a comparison of resource consumption with baseline methods?  \n\n3. Sec. 6 mentions BMW's limitations but lacks in-depth discussion. Can the authors elaborate on BMW's performance under noisy data conditions and in cross-domain transfer scenarios?", "limitations": "Yes, the paper briefly mentions in Sec. 6 BMW's sensitivity to noisy data and challenges in cross-domain transfer, but the discussion remains superficial.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## 1. Paper Quality (Quality)\n\nThe paper proposes a novel memory bank rewriting mechanism (BMW) to enhance the performance of unsupervised person re-identification (ReID), demonstrating a certain level of innovation. Experimental results show that BMW performs exceptionally well on standard benchmarks (Market-1501 and MSMT17), surpassing methods with stronger backbone networks, which validates the approach's effectiveness. Through ablation studies, the paper thoroughly analyzes the roles of each component in BMW, providing robust supporting evidence. However, the theoretical derivation section is lacking, particularly in justifying the advantages of BMW, where the reasonableness of assumptions and the rigor of the derivation require further refinement. While the experimental section highlights performance gains, it omits discussion on computational complexity and model efficiency—key factors for practical applications. Additionally, the specific implementation details of BMW are not clearly described; for instance, the derivation and application of Eqn. 8 lack sufficient explanation, hindering reader comprehension.\n\n## 2. Paper Clarity (Clarity)\n\nThe paper is well-organized with a coherent logical flow, making it easy to follow from the introduction to the experimental results. The use of figures and formulas is appropriate, effectively illustrating BMW's working principles and experimental outcomes. However, some terms and symbols lack clear definitions; for example, in Sec. 4, the definition and updating method of M[c] need more detailed elaboration. The related work section does not adequately distinguish BMW from existing methods, which diminishes the visibility of its innovation. Furthermore, the use of images in the paper is limited; additional figures could better highlight the innovation or clarify complex formulas.\n\n## 3. Paper Significance (Significance)\n\nBMW enhances the memory bank's representation and discrimination capabilities through a bidirectional rewriting mechanism, offering a fresh perspective for unsupervised ReID research. In scenarios where data annotation is expensive, BMW provides an effective solution, contributing to advancements in unsupervised learning. However, the paper falls short in validating BMW's generalization ability, such as its effectiveness in transfer learning across different datasets or tasks, with insufficient evidence provided. The discussion of potential challenges in practical applications, such as scalability on large-scale datasets, is also not comprehensive enough.\n\n## 4. Paper Originality (Originality)\n\nBMW is the first to unify intra-class and inter-class constraints in memory bank optimization, presenting an innovative mechanism. The novel design of the inter-class constraint objective function effectively boosts the memory bank's discrimination capability, showcasing its uniqueness. However, the comparison with related work is insufficiently detailed, failing to emphasize BMW's distinct contributions, which reduces the prominence of its originality. Additionally, some experimental designs and result presentations resemble existing literature, slightly undermining the innovation.", "was_revised": true, "final_justification": "The authors have thoroughly addressed all my concerns, particularly regarding the balance between intra-class and inter-class constraints, computational efficiency, and robustness to noisy labels. The additional experiments showcasing the superiority of their BMW method in both unsupervised and supervised settings further strengthen the paper. Moreover, the method’s ability to optimize feature representations and generalize across diverse datasets adds substantial value. Based on these improvements and clarifications, I recommend accepting the paper.", "rebuttal": "**Response to the Reviewer p7UF**\n\n-----\nThank you very much for your time and insightful comments.\nWe sincerely appreciate your recognition of the novelty of this paper.\nThe point-by-point detailed response and the revision plans to each concern are given as follows:\n\n---\n**Q1:** Balance between the intra-class and inter-class constraints.\n\n**A1:** Thanks for the insightful question. The two constraints are balanced with two loss weights, *i.e.*, $\\lambda _ {intra}$ for the intra-class constraint $\\mathcal{L} _ {intra}$, and $\\lambda _ {inter}$ for the inter-class constraint $\\mathcal{L} _ {inter}$. The values of $\\lambda _ {intra}$ and $\\lambda _ {inter}$ should be different due to their distinct roles: \n- The term $\\mathcal{L} _ {intra}$ encourages memory banks to approach corresponding positive training samples, a larger $\\lambda _ {intra}$ enhances the intra-class constraint and encourages the memory banks to closely represent their positive samples while maintaining discriminative power in feature space.\n- In the meantime, since $\\mathcal{L} _ {inter}$ pushes memory banks away from each other, a larger $\\lambda _ {inter}$ may push memory banks far away from corresponding positive samples.\n\nTherefore, $\\lambda_{inter}$ should be smaller than $\\lambda_{intra}$ from the above analysis.\n\nWe also conduct the hyper-parameter analysis on both Market-1501 (as shown in our manuscript) and DukeMTMC-reID (as shown in our response to the Reviewer a7FY). \nThe analysis results on Market-1501 and DukeMTMC-reID show that:\n 1. The configuration ($\\lambda _ {intra}=0.9$, $\\lambda _ {inter}=0.2$) achieves peak performance on both datasets. This indicates that hyperparameters selected on one dataset can be applied to others without sacrificing the performance. \n 2. The superior performance does not totally rely on careful selection of hyperparameters. We notice that BMW consistently achieves better results than others as long as $\\lambda _ {intra}$ sits in $[0.6,1]$ and $\\lambda _ {inter}$ in $[0.1,0.25]$.\n\nConsequently, the hyperparameters are easily tunable for superior performance across different datasets.\n\nBy the way, the superiority range of $\\lambda_{inter}$ is smaller than that of $\\lambda _ {intra}$. This is because $\\mathcal{L} _ {intra}$ decreases after several training iterations even when using the hard sampling strategy, while $\\mathcal{L} _ {inter}$ stays large because of the large number of identities. We can apply a threshold on $\\mathcal{L} _ {inter}$ to ignore memory bank pairs that are already far away from each other. This strategy will enlarge the operating range of $\\lambda_{inter}$.\n\nThese will be added in our revised version for a clear explanation. Meanwhile, a discussion on the loss weights and sampling strategy has been provided in the section of \"Limitations and future research\" in our manuscript.\n\n---\n\n**Q2:** Computational complexity and model efficiency.\n\n**A2:** Thanks for the comments. We provide the following clarification for training and inference:\n\n- Training: In each training batch, we randomly sample 16 identities, *i.e.*, clusters by DBSCAN. For each sampled identity, 16 images are randomly selected to compose the training batch. When computing the inter-class constraint ($\\mathcal{L} _ {inter}$) for the training batch, 16 memory banks corresponding to those sampled 16 identities are updated by their closest memory bank vector (hard sampling strategy) via $\\mathcal{L} _ {inter}$. Compared with the baseline model, our BMW method introduces two extra steps:\n  1. Finding the closest memory bank vector for 16 memory banks. This costs less than 1ms for the memory banks with a size of less than 10000 (only around 1000 in our experiments on person ReID datasets) with the Intel(R) Core(TM) i7-14700 CPU.\n  2. Computing the update vector for the 16 memory banks by $\\mathcal{L} _ {inter}$. As shown in Eqn.(11), computing the update vector for each memory bank only introduces one vector dot-product, one vector addition, one vector scaling, and one scalar addition. This only needs 4096 multiplications and 2049 additions with 2048 feature dimensions in our paper and also costs less than 1ms with CPU (will be faster with GPU).\n\n  Therefore, our method only introduces negligible overhead compared with the baseline method. We also test the training time overhead: training the baseline model costs 3h25m33s, whereas ours costs 3h26m53s (just an extra 80s over roughly 3.5h).\n\n- Inference: As our method only updates the memory bank used in the training process and only the backbone is used to extract image features in the inference process, our method does not affect the inference efficiency.\n\nThese will added in the final version for the clarification of the model efficiency.\n\n---\n\n**Q3:** More in-depth discussions about the limitation.\n\n**A3:** Thanks for your suggestion. We will provide more discussions about the limitations as follows:\n\n-  Noisy data:  \n   Our method is applied to the unsupervised person ReID task. As the pseudo label is generated by unsupervised clustering method, *i.e.*, DBSCAN, the training process is performed with noisy in the label. Along with the model training, the discrimination ability of the model is progressively enhanced. The pseudo label generated by DBSCAN based on extracted features will be also progressively more precise, which in turn helps the model training. However, the noisy label will mislead both the model training and the memory bank optimization. Therefore, for future improvement, a robust memory bank optimization method should be studied that better captures relationships between memory entries and training samples to improve effectiveness.\n  \n- Cross-domain scenarios:  \n   Our experiments on Market-1501, MSMT-17 (reported in the manuscript), and DukeMTMC-reID (detailed in Response to Reviewer a7FY) demonstrate both strong performance and consistent hyperparameter transferability across datasets. While current unsupervised person ReID methods typically train and test on the same dataset, cross-domain evaluation reveals significant performance degradation due to distribution shifts. \n   \n   Recent work in lifelong person ReID has begun addressing this challenge through memory bank-based knowledge preservation. Current approaches primarily optimize memory banks using only positive sample relationships. We observe that incorporating inter-class constraints - as in our BMW framework - could significantly enhance knowledge representation. Building on this insight, future research should investigate BMW-based memory bank optimization to improve performance in lifelong ReID scenarios. This direction could potentially bridge the gap between single-domain and cross-domain ReID performance.\n\n   These discussions will be added in the revised version.\n\n\n---\n\n**Other issues in comments**\n\n- Details for the derivation:  \n  We provide details of the derivation for Eqn.(10) as follows:\n  \n  - $\\frac{\\partial \\mathcal{L}_{intra}(\\mathcal{M}[c],f_i)}{\\partial\\mathcal{M}[c]}$  \n  $=\\frac{1}{8}\\times4\\times\\|\\mathcal{M}[c]-f_i\\|_2^3\\times\\frac{\\mathcal{M}[c]-f_i}{\\|\\mathcal{M}[c]-f_i\\|_2}$  \n  $=\\frac{1}{2}\\|\\mathcal{M}[c]-f_i\\|_2^2(\\mathcal{M}[c]-f_i)$  \n  $=\\frac{1}{2}(\\|\\mathcal{M}[c]\\|_2^2+\\|f_i\\|_2^2-2(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$  \n  $=\\frac{1}{2}(1+1-2(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$  \n  $=(1-(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$,\n\n\n  - $\\frac{\\partial \\mathcal{L}_{inter}(\\mathcal{M}[i],\\mathcal{M}[j])}{\\partial\\mathcal{M}[i]}$  \n  $=\\frac{1}{8}\\times4\\times\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2^3\\times\\frac{\\mathcal{M}[i]+\\mathcal{M}[j]}{\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2}$  \n  $=\\frac{1}{2}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2^2(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n  $=\\frac{1}{2}(\\|\\mathcal{M}[i]\\|_2^2+\\|\\mathcal{M}[j]\\|_2^2+2(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n  $=\\frac{1}{2}(1+1+2(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n  $=(1+(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$.\n  \n  This will be added in our revised version for clarity.\n\n- Comparison with related works:  \n  We will expand the discussion of related literature in the revised version, including recent advances such as, FUReID \\[PR 2024\\], IIDS \\[T-PAMI 2024\\], FuseDSI \\[IF 2024\\], RTMen \\[T-IP 2023\\], ISE \\[CVPR 2022\\], P $^2$ LR \\[AAAI 2022\\], and IICS \\[CVPR 2021\\]. Additionally, we provide supplementary comparison results on the DukeMTMC-reID dataset in our response to Reviewer a7FY.\n\n- Scalability on large-scale datasets:  \nAbout the overhead on large-scale datasets: As discussed in our response \"A2\", our method only introduces negligible overhead compared with the baseline model.  \nAbout the optimization effectiveness, for datasets with a large number of identities, the inter-class loss may remain elevated during training. Therefore, we need to adjust inter-class constraints. Therefore, for future research, a dynamically weighting strategy and a progressively hard sampling strategy could be studied to enhance the robustness and effectiveness of our method. This will be added as limitations and future research in the revised version.\n\n- Figures:  \n  We will enhance the color contrast, add more textual notes for figures. An additional figure of our framework will be added in the revised version.\n\n---\n\nWe believe these responses and revisions will address your concerns and those updated content will significantly strengthen our paper.\n\nThank you again for your recognition of the novelty of our paper and invaluable feedback."}, {"review_id": "pl7slUCMMA", "reviewer": "Reviewer_dA3y", "summary": "A novel method, Bidirectionally Memory bank reWriting (BMW), is proposed for unsupervised person reID. The overall structure is based on the Memory Bank approach for unsupervised person reID through contrastive learning. The key problem to be tackled is that existing approaches with the memory bank contrastive learning mainly focus on intra-class compactness. This paper introduces the inter-class constraint into the loss for the model training. In such a way, it would improve the feature discriminability. Instead of the common momentum mechanism used in the memory bank update, this paper redefines the memory bank update through a gradient descent update. Experiments show that BMW achieves state-of-the-art performance on benchmarks like Market-1501 and MSMT17.", "questions": "1. Training oscillations could be an issue. In a single batch, due to training samples coming from the different classes, the memory bank update in each round may be affected by multiple negative classes. At the same time, these classes may have an impact on each other.  This could cause the memory update to be unstable. If the data class imbalance and fake negative (caused by label noise) exist, such an issue could be more serious. \n\n2. Current dynamic weighting is on the batch level. It cannot sense the overall distribution of the whole dataset. If a certain class is always \"easy\" but not \"hard\" sample. The corresponding class information inside the memory bank may not be optimised properly. \n\n3. In Equation 4, Lambda_intra and Lambda_inter settings are sensitive to the performance. For example, larger lambda_inter could cause training to be unstable. A more robust/dynamic way for determining these two parameters will be helpful.\n\n4. The equation 10 is a function of the power of 4. It seems that equation 11 (differentiation of equation 10) is wrong. I cannot see the right derivation over here.\n\n5. There is no explanation on Fig 2. \n\n6. There are some typos on line 226. \n\n7. In line 339, it said BMW outperforms CaCL by 2.4% on MSMT17 in terms of mAP. This is different from the information on Table 3.", "limitations": "See the comments above in \"Weaknesses\" and \"Questions\"", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1) This paper introduces inter-class constraints into the contrastive learning for memory bank update through gradient descent. \n2) The power of 4 of Euclidean distances (Equation 10) is introduced to measure the positive pair and the negative pair in the contrastive learning for updating the loss in the intra-class constraint loss and the inter-class constraint loss. This update helps dynamically update the weighting to reflect the importance of each training sample on its contribution to memory bank update. It will make the training focus on the training samples causing larger differences (i.e. hard samples).\n3) Comprehensive ablation studies are introduced with reasonable visualisations and theoretical proof provided. \n\nWeaknesses:\n\n1) The dynamic weighting introduced in this paper is to reflect the different importance of each training sample on updating the memory bank. However, in terms of the loss function design (Equation 4), the weightings are manually tuned and not automatically adapted based on data distribution or training dynamics. This could be an issue if the dataset has the problem of class imbalance. \n2) The proposed BMW should have a contribution to any model based on Memory Bank through contrastive learning. It will be helpful to carry out other experiments beyond person reID application.\n3) There should be more new methods in the past 2 years (ie 2024 & 2025) for comparison. Or some more popular method will be appreciated such as Cluster Contrast.", "was_revised": true, "final_justification": "The authors have addressed the key concerns raised in my reviews. I am happy to lift the scores from \"3: Borderline reject\" to \"4: Borderline accept\".", "rebuttal": "**Response to the Reviewer dA3y**\n\n----\n\nWe sincerely appreciate your recognition of the novelty of this paper. And we thank you very much for detailed and constructive comments.\nThe point-by-point detailed response and the revision plans to each of your concern are given as follows:\n\n---\n**Q1 & Q3:** Training oscillations and settings of loss weights.\n\n**A1:** Thanks for the insightful question! We would like to respond to your Q1 on training oscillations and Q3 on the loss weight settings together.\n\nTo chase a stable training, we use two loss weights $\\lambda _ {intra}$ and $\\lambda _ {inter}$ for intra-class and inter-class constraints to control the scale of memory bank update. \n\nTheoretical analysis indicates that $\\lambda _ {intra}$ could be a large value and $\\lambda _ {inter}$ should be small. The reasons are: \n- Because $\\mathcal{L} _ {intra}$ encourages memory banks to approach corresponding positive training samples, a larger $\\lambda _ {intra}$ will emphasize the intra-class constraint and make memory banks similar to those positive samples, but still discriminative in the feature space. \n- While, $\\mathcal{L} _ {inter}$ pushes memory banks away from each other, and a larger $\\lambda _ {inter}$ may push memory banks far away from corresponding positive samples.\n\nOur method can also be regarded as an enhancement of the intra-class constraint. Therefore, the selection of loss weights can start from $\\lambda _ {intra}=1$ and $\\lambda _ {inter}=0.1$, where our BMW achieves 84.9% mAP on Market-1501, already outperforming most other methods. So, it's not very difficult to select the loss weights in our method. And like any other gradient-based optimization method, an excessively large loss weight can lead to model collapse.\n\nAs shown in the experiments on Market-1501 (in our paper) and DukeMTMC-reID (in our response to Reviewer a7FY), the superior performance **does not totally rely on careful selection of loss weights**. We notice that BMW consistently achieves promising performance as long as $\\lambda_{intra}$ sits in $[0.6,1]$ and $\\lambda_{inter}$ in $[0.1,0.25]$.\n\nAbout class imbalance: In each training batch, we randomly sample 16 identities from the dataset, *i.e.*, clusters by DBSCAN. For each sampled identity (cluster), 16 images are randomly sampled from its cluster to compose the training batch. This sampling strategy ensures that every identity (cluster), regardless of its original cluster size, contributes equally to each mini-batch, thereby alleviating the class-imbalance issue.\n\nAbout noisy label: For the unsupervised person ReID task, the pseudo label generated by DBSCAN contains certain noise. Along with the model training, the discrimination ability of the model is progressively enhanced. The pseudo label generated by DBSCAN based on extracted features will be also progressively more precise, which in turn helps the model training. The noisy label does mislead both the model training and the memory bank optimization, however, the experiments show that it's fine for these datasets. For future improvement on larger datasets, a robust memory bank optimization method with considering more relationships among memory banks and training samples could be studied to enhance the robustness against label noise.\n\nAdditionally, as we have discussed in the section \"Limitations and future research\", the following methods for selecting loss weights could be studied in the future to enhance the robustness and effectiveness of the memory bank rewriting:\n 1. Enhancing the dynamically weighting strategy to be aware of the distribution of different datasets.\n 2. A progressively sampling strategy w.r.t different stages of the model training.\n 3. A distribution metric to guide the loss weight settings.\n\n---\n\n**Q2:** Dynamic weighting and optimization for \"easy\" memory banks.\n\n**A2:** Thanks for the constructive comments. We clarify that, the dynamic weighting is not totally on the batch level.\n\nWe would like to first describe the detailed steps of intra-class and inter-class constraints as follows:\n1. An unsupervised clustering method, *e.g.*, DBSCAN in our paper, is applied to cluster all the samples into different clusters based on extracted features before model training. \n2. A memory bank is initialized for each cluster with the average vector of all L2-normalized features in this cluser and a L2 normalization.\n3. In each training batch, we randomly sample 16 identities from the dataset, *i.e.*, clusters by DBSCAN. For each sampled identity (cluster), 16 images are randomly sampled from its cluster to compose the training batch.\n4. 16 memory banks corresponding to those sampled 16 identities (clusters) are updated by intra-class and inter-class constraints as:\n    - For the intra-class constraint ($\\mathcal{L} _ {intra}$), each memory bank is updated with its hard positive sample (the sample exhibiting  the smallest similarity to the memory bank, selected from the 16 samples in the training batch) via Eqn.(11).\n    - For the inter-class constraint ($\\mathcal{L} _ {inter}$), each memory bank is updated with its hard negative memory bank vector (the memory bank vector exhibiting the largest similarity to this memory bank, selected from all the memory banks except this one) via Eqn.(11).\n\nThe dynamic weighting could capture the overall distribution because:\n- For the intra-class constraint, 16 samples are randomly sampled for each cluster, which could be enough to provide an approximate reflection of the entire sample distribution in this cluster. For instance, there are roughly 20 samples in each cluster in the training process on Market-1501. \n- For the inter-class constraint, the hard negative sample is selected from all memory banks of the dataset, *i.e.*, it is class-wise. Thus, it can also sense the overall data distribution.\n\nWe would also like to clarify that, the \"hard\" or \"easy\" label for sampling in the inter-class constraint is used to describe the relationship between two memory banks, instead of a single memory bank vector.\n\nAbove explanations will be added in the final version for clarity.\n\n---\n\n**Q4:** The derivation about Eqn.(10).\n\n**A4:** Thanks for pointing out that. We provide details of the derivation for Eqn.(10) as follows:\n  \n  - $\\frac{\\partial \\mathcal{L}_{intra}(\\mathcal{M}[c],f_i)}{\\partial\\mathcal{M}[c]}$  \n$=\\frac{1}{8}\\times4\\times\\|\\mathcal{M}[c]-f_i\\|_2^3\\times\\frac{\\mathcal{M}[c]-f_i}{\\|\\mathcal{M}[c]-f_i\\|_2}$  \n$=\\frac{1}{2}\\|\\mathcal{M}[c]-f_i\\|_2^2(\\mathcal{M}[c]-f_i)$  \n$=\\frac{1}{2}(\\|\\mathcal{M}[c]\\|_2^2+\\|f_i\\|_2^2-2(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$  \n$=\\frac{1}{2}(1+1-2(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$  \n$=(1-(\\mathcal{M}[c])^\\top f_i)(\\mathcal{M}[c]-f_i)$,\n\n\n  - $\\frac{\\partial \\mathcal{L}_{inter}(\\mathcal{M}[i],\\mathcal{M}[j])}{\\partial\\mathcal{M}[i]}$  \n$=\\frac{1}{8}\\times4\\times\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2^3\\times\\frac{\\mathcal{M}[i]+\\mathcal{M}[j]}{\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2}$  \n$=\\frac{1}{2}\\|\\mathcal{M}[i]+\\mathcal{M}[j]\\|_2^2(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n$=\\frac{1}{2}(\\|\\mathcal{M}[i]\\|_2^2+\\|\\mathcal{M}[j]\\|_2^2+2(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n$=\\frac{1}{2}(1+1+2(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$  \n$=(1+(\\mathcal{M}[i])^\\top \\mathcal{M}[j])(\\mathcal{M}[i]+\\mathcal{M}[j])$.\n  \nThis will be added in our revised version for clarity.\n\n---\n\n**Q5:** Explanation on Fig. 2.\n\n**A5:** Thanks! We will provide a detailed caption for Fig. 2 as follows:\n\n    \"Figure 2: Illustration of the advantage in inter-class constraint of the proposed objective function in Eqn.(8) w.r.t. different angles between $\\mathcal{M}[i]$ and $\\mathcal{M}[j]$. Yellow arrows and blue arrows denote $\\mathcal{M}[i]$ and $\\mathcal{M}[j]$, respectively. Green dashed arrows and red dashed arrows denote the updated $\\mathcal{M}[i]$ by the proposed objective function in Eqn.(8) and the previous method, respectively. It can be observed that the objective function in Eqn.(8) is more effective in pushing $\\mathcal{M}[i]$ away from $\\mathcal{M}[j]$.\"\n\nThis will be added in the revised version for clarity.\n\n---\n\n**Q6:** Typos in Line 226\n\n**A6:** Thanks for pointing out that! We have carefully proofread the manuscript and fixed it as:\n\n - \"In this section, we first analyze the effect of hyper-parameters $\\lambda _ {intra}$, $\\lambda _ {inter}$ and $\\tau$ on Market-1501\"\n\nTypos will be corrected in the final version.\n\n---\n\n**Q7:** Mistake about the performance comparison.\n\n**A7:** Thanks for pointing out that. 2.4% is the mAP improvement by our BMW compared with PPLR $^ {``\\dag\"}$ on MSMT-17. We mistakenly took it as the one compared with CaCL. We have corrected it as:\n\n- \"For example, BMW outperforms the recent SOTA method CaCL [2] by 1.6% and 8.1% in mAP on Market-1501 and MSMT-17, respectively\"\n\nThis will be corrected in the revised version.\n\n---\n\n**Other issues in comments**\n\n- Other tasks beyond person ReID:  \nThanks for the constructive suggestion! We plan to evaluate our method on other tasks like vehicle ReID (waiting for its authors' response regarding the data access) and self-supervised learning in the future study. By the way, we have supplemented the experimental results on DukeMTMC-reID in our response to Reviewer a7FY.\n\n- More related works:  \nSure! We will add more recent and popular works in the revised version, *e.g.*, FUReID \\[PR 2024\\], IIDS \\[T-PAMI 2024\\], FuseDSI \\[IF 2024\\], RTMen \\[T-IP 2023\\], ISE \\[CVPR 2022\\], P $^2$ LR \\[AAAI 2022\\], and IICS \\[CVPR 2021\\]. By the way, the Cluster Contrast method is already cited as \\[15\\] and  serves as the baseline model in our paper.\n\n---\n\nWe believe that these responses and revisions will address your concerns and those updated content will significantly strengthen our paper.\n\nThank you again for your recognition of the novelty of our paper and for your invaluable feedback."}]

Author rebuttal:

Meta-review: This paper proposes BMW, a novel bidirectional memory bank rewriting mechanism for unsupervised person Re-ID that incorporates both intra-class and inter-class constraints through a gradient descent update framework. All four reviewers consider the paper's technical soundness and novelty. Reviewers RyMZ and p7UF strongly recommended acceptance, with clear motivation and comprehensive experiments. Reviewers a7FY and dA3y initially had concerns about dataset coverage, implementation details, and hyperparameter sensitivity, but both upgraded their scores to borderline accept after the authors provided convincing responses.
Decision: Accept (poster)