Title: BMW: Bidirectionally Memory bank reWriting for Unsupervised Person Re-Identification
Venue: NeurIPS 2025
Accepted: yes
Keywords: Memory Bank

Unsupervised Person Re-Identification
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 reWriting for Unsupervised Person Re-Identification

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, BMWformulates 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.

1 Introduction

Person Re-Identification (ReID) aims to match a query person image in a gallery set, making it an important task in intelligent surveillance systems [1-4]. Currently, supervised person ReID methods have been widely studied from different aspects and achieved promising performance [514]. 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

Baiwei Guo

Huawei Technologies Ltd. Shanghai, China ❜❛✐✇❡✐✳❣✉♦❅❡♣❢❧✳❝❤

Jing Yuan ∗

♥❦②✉❛♥❥✐♥❣❅❣♠❛✐❧✳❝♦♠

w/o inter-class

constraint

w/ inter-class

constraint

inter-class constraint

intra-class constraint

focus on unsupervised person ReID, and multiple methods are proposed from different aspects, such as pseudo label prediction [15, 16], unsupervised metric learning [17, 18], and image synthesis [19, 20]. More details of related works are summarized in Sec. 2.

Currently, state-of-the-art unsupervised person ReID methods adopt the contrastive learning framework based on memory banks [17, 21-26]. 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 momentum mechanism, i.e. , rewritten memory banks are weighted summation of the initial ones and positive samples. Features are optimized by contrastive loss to 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 to supervise the discrimination capacities of features. However, the previously used momentum mechanism for memory bank rewriting solely focuses on 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 on those memory banks. Secondly, compared with model parameters that are entirely updated in each iteration, only several memory banks corresponding to selected clusters are rewritten once in each iteration. This requires an effective objective formulation to enhance the separability with few rewriting steps. Existing metric learning methods commonly formulate the inter-class separability constraint as 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 ReID 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. 1, the extra inter-class constraint enhances the separability of memory banks, thus it further enables effective feature optimization. We further propose an 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 reWriting (BMW). We test our BMW on standard benchmarks including Market-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, BMWboosts the mAP accuracy from 81.2% to 86.3% on Market-1501, outperforming existing both unsupervised and transfer learning methods by a clear margin. Moreover, BMW even outperforms recent DCC [23] which uses stronger backbone, i.e. , 86.2% by BMW with ResNet50 v.s. 85.8% by DCC with ResNet50-ibn on Market-1501.

The contributions of this paper can be summarized as follows:

1) To the best of our knowledge, this is the first effort to 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 to the target data. Some works targeting to narrow the domain gap between the source data and the target data in image space by GANs [27, 28, 19, 29, 30, 20, 31]. 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.

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 [21, 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, 16] 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 a graph consistency constraint between student models and momentum average models for unsupervised model training. Some works adopt additional cues to boost performance [40-45]. For examples, Fu et al. [43], Zhang et 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 or selected features for the contrastive loss computation. Currently, memory bank based methods dominate the unsupervised person ReID task and achieve promising performance [21, 25, 18, 17, 22, 49, 50, 23, 51, 24]. For examples, 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 [23, 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 reWriting (BMW). Experiments show that the proposed BMWoutperforms existing methods by a clear margin.

3 Overview

Given an unlabeled dataset D , unsupervised person ReID aims to learn the ReID model on D without annotation. D can be denoted as { x i | i = 1 ...N } . x i and N denote the i -th image and the number

of images in D , respectively. We denote the feature extraction as f i = Φ( x i , θ ) . Φ and θ denote the CNN feature extractor and its parameters, respectively.

We adopt the contrastive learning framework based on 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, a 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 are used to initialize a memory bank M , i.e. , M [ c ] is initialized by the centroid feature of the c -th cluster with L2 normalization.

Via treating M as a non-parametric classifier, the contrastive loss is used for model training following previous works [23, 22, 35]. For x i belonging to the c -th cluster, the contrastive loss is computed as:

where τ is temperature hyper-parameter to adjust the scale of similarity distribution. Along with the model training, M is rewriting based on both training samples and itself by the proposed BMW method, which will be given in Sec. 4.

4 Bidirectionally Memroy bank reWriting

Existing methods rewrite M with positive training samples via the momentum mechanism. For example, it rewrites M [ c ] with f i belonging to the c -th cluster can be formulated as:

where α is used to adjust the update rate of memory banks.

In this paper, we propose a novel perspective of memory bank rewriting, which formulates the rewriting procedure as the gradient decent update for objective function L M as follows:

Note that the update rate is omitted in Eqn. 3 and will be used for each constraint of L M . To enhance both the representation and the discrimination capacities of M , the proposed BMW formulates L M as the combination of both the intra-class constraint L intra and the inter-class constraint L inter as:

where λ intra and λ inter denotes loss weights. Then Eqn. 3 can be rewritten as:

To enhance the rewriting efficiency, the hard sampling strategy is used to compute L M , i.e. , the farthest positive sample in each training batch and the closest negative memory bank vector for M [ c ] are used to compute L intra and L inter , respectively.

4.1 Intra-class Constraint

L intra aims to enhance the representation capacity of M , which can be designed to reduce the distance between M and corresponding positive sample. Commonly, multiple samples of one selected cluster are used in a training batch and a cluster commonly contains a limited number of samples, e.g. , 16 samples of one cluster are sampled in a training batch in previous works [53, 22] and less than 30 samples in each cluster. Thus, L intra 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 i belonging to the c -th cluster can be formulated as:

Its gradient w.r.t. M [ c ] is computed as:

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Memory banks is encouraged to approach positive samples by λ intra to acquire the feature updates along with the update of θ .

It is worth noting that setting λ intra = 1 -α and λ inter = 0 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

L inter aims to enhance the discrimination capacity of M . It can be intuitively designed to enlarge the distance between M and negative samples in D . However, only a small number of clusters are selected in each training batch compared to the total number of clusters, e.g. , only 16 clusters are sampled in a single training batch in previous works [53, 22] and there are more 500 clusters in D . Thus, a training batch only contains limited information for the global inter-class constraint. We hence design L inter 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: -1 2 ||M [ i ] -M [ j ] || 2 2 . Its gradient w.r.t. M [ i ] is M [ j ] -M [ i ] . Then, the updated M [ i ] with only inter-class constraint is (1 + λ inter ) M [ i ] -λ inter M [ j ] , whose norm is larger than 1 for λ inter ∈ [0 , 1] 2 . Thus, the distance is enlarged by both enlarging the norm of M [ i ] and the angle between M [ i ] and M [ j ] . As M [ i ] is 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 L inter to pull M [ i ] close to the opposite vector of M [ j ] , which is formulated as:

Its gradient w.r.t. M [ i ] is computed as:

For the iter-class constraint, rewriting with Eqn. 9 is more effective in enlarging the angle between M [ i ] and M [ j ] , i.e. , enlarging the distance between them after L2 normalization. Fig. 2 shows the advantages of the proposed objective function for L inter in Eqn. 8. We also provide a theory proof of the advantage of Eqn. 8 for one step update in Sec. A.1.

4.3 Dynamically Weighting

Directly using the Euclidean distance in Eqn. 6 and Eqn. 8 is not 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, we

weight Eqn. 6 and Eqn. 8 by 1 4 ||M [ c ] -f i || 2 2 and 1 4 ||M [ i ] + M [ j ] || 2 2 , respectively. Then L intra and L inter are re-formulated as:

And their gradient w.r.t. M [ c ] and M [ i ] are computed as (details are provided in A.2):

This weighting strategy focuses on both hard positive pairs, i.e. , the pair f i and M [ c ] having small cosine similarity, and hard negative pairs, i.e. , the pair M [ j ] and M [ i ] having large cosine similarity. Eqn. 11 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 6 cameras at 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 as query images and remaining 19,732 images are used as 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. In 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-reID [55] are provided on the repository on github. Vehicle ReID task [56-59] is similar with the person ReID 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 Rank10 accuracies are reported.

5.2 Implementation Detail

The proposed model adopts ResNet50 [61] pre-trained on ImageNet [62] as the feature extractor Φ . 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], a GEM pooling followed by batch normalization layer [64] and L2-normalization layer is added. Experiments with other backbones are provided in Sec. A.3. Parameter τ in Eqn. (1) is set to 0.05 following [22]. λ intra , and λ inter are set as 0.9, and 0.02, respectively. More details are provided in Sec. A.4.

5.3 Model Analysis

In this section, we first analysis the effect of hyper-parameters λ intra , λ intra and τ on Market-1501 by varying the value of one parameter and keeping others fixed to 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

λ intra affects the update rate for memory banks approaching positive training samples, and thus affects the representation capacity of memory banks. The result of evaluation on λ intra is shown in Fig. 3. It can be observed that the performance is insensitive to λ intra in an appropriate rage from 0.8 to 0.9. And the best performance is achieved when setting λ intra to 0.9. A larger value of λ intra gives more emphasis to the intra-class constraint and makes L inter neglected by L M . While, a small

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value of λ intra releases the constraint on the representation capacity of memory banks and limits the compactness of intra-class samples.

λ inter affects the update rate for pushing memory banks away from each other, and thus affects the discrimination capacity of memory banks. The result of evaluation on λ inter is shown in Fig. 4. It can be observed that the best performance is achieved when setting λ inter to 0.2. A larger value of λ inter gives more emphasis to the inter-class constraint and makes L intra neglected by L M . While, a small value of λ inter releases the constraint on

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the discrimination capacity of memory banks and thus limits the separability of inter-class samples. Compared with the value of λ intra , λ inter 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 λ intra is needed to adapt to the feature update of training samples along with the update of θ . The balance between λ intra and λ intra is discussed in Sec. A.6.

τ affects the computation of L θ by adjusting the distribution of similarities between training samples and memory banks. The result of evaluation on τ is shown in Table 1. It is clear that setting τ to 0.05 achieves the best performance.

5.3.2 Ablation study

Results of the ablation study on the proposed inter-class constraint and the proposed dynamically weighting strategy is summarized in Table 2. 'baseline [22]' denotes the baseline method following the same settings in [22], and neither the interclass constraint or dynamically weighting strategy is used in the baseline. 'w/o L inter ' denotes only the intra-class constraint and the dynamically weighting strat-

egy are used. ' ||M [ i ] - M [ j ] || 2 2 in Eqn. 8' denotes using the Euclidean distance between two memory banks for the inter-constraint objective function as in previous methods.

It can be observed from Table 2 that the proposed inter-class constraint effectively boosts the performance by a large margin, e.g. , improving the mAP from 81.5% to 86.3%. It is worth noting that the improvement of L inter is based on a strong baseline that achieves 81.5% mAP. This indicates that the separability of memory banks is important for discriminative ReID feature learning, which is neglected in previous methods. This also demonstrates that the proposed inter-class constraint is able to effectively enhance the ReID feature optimization by completing the constraints of the objective function for the memory bank rewriting.

The proposed novel formulation in Eqn. 8 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 [ i ] -M [ j ] || 2 2 version. The comparison results clearly demonstrate that, with only one update step, the proposed formulation in Eqn. 8 is more effective in enlarging the angle between two L2 normalized features in memory banks.

As shown in Table 2, the proposed dynamically weighting strategy further boosts the accuracy of the ReID model from a rather high performance, e.g. , it boosts the mAP accuracy from 85.0% by 1.3%. Note that, compared to 'baseline [22]', 'w/o L inter ' only additionally uses the proposed dynamically weighting strategy and achieves a better performance, e.g. , 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.

To further illustrate the effectiveness of each component in this paper, we compare the training procedures of the ablation study in Fig. 5. 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. It can be observed that the method without the intra-class constraint performs the worst, which indicates that ensuring the separability of memory banks via the proposed intra-class constraint enables the model to gain additional discrimination capacity for the ReID task. It can also be observed that, compared with the previously used Euclidean distance in the inter-class constraint, the proposed new formulation in Eqn. 8 is more effective for memory bank rewriting within one step.

We also evaluate different sampling strategies including random sampling, average sampling and easy sampling for computing Eqn. 4. The training procedures w.r.t. different strategies are shown in Fig. 6. It can be 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 strategy performs similar with the hard sampling strategy because it can also selects hard samples at times. It clear that the hard sampling strategy performs the best through the entire training procedure.

5.4 Comparison with SOTA Methods

The comparison results one both Market-1501 and MSMT17 with SOTA methods are summarized in Table 3. It's clear that the proposed BMW outperforms existing SOTA methods by a clear margin. For example, BMW outperforms the recent SOTA method CaCL [2] by 1.6% and 2.4% in 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 [18] and MMT [38], by a large margin. This indicates that BMW is more effective for discrminative feature optimization for the ReID task.

Compared with recent methods that use the momentum mechanism to rewrite memory banks [23, 22, 18, 17], 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 backbone, or 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.

6 Conclusion

This paper proposes a 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 on intra-class compactness while neglecting inter-class separability. By formulating the rewriting process as a 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 on 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.

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A Appendix

A.1 Proof of the advantage of Eqn. 8

𝑌

𝑦

𝑆𝐼

𝑥

𝑆𝐼𝐼𝐼

𝑆𝐼𝐼

Theorem 1. Let x, y ∈ R n be L2 normalized, i.e., ∥ x ∥ 2 = ∥ y ∥ 2 = 1 . Consider 0 < λ < 1 and the following vectors

Let θ ( y,a ) and θ ( y,b ) be respectively the angle between y and a and that between y and b . Then, θ ( y,b ) ≥ θ ( y,a ) .

̸

Proof. We only need to consider the case where y = x . Let X be the space expanded by the vector x , proj( y, X ) be the projection of y on X and y ⊥ = y -proj( y, X ) = 0 . Since x and y ⊥ / ∥ y ⊥ ∥ are orthogonal and normalized, there exists an orthonormal basis expanded by these two vectors. Therefore, there exists one isometry A such that A x = [1 , 0 , 0 , ..., 0] ∈ R n and A ( y ⊥ / ∥ y ⊥ ∥ ) = [0 , 1 , 0 , ..., 0] ∈ R n . Recall that isometry preserves norms and angles. In the following, we focues on θ ( A y, A b ) , θ ( A y, A a ) and show that θ ( A y, A b ) > θ ( A y, A a ) .

̸

Since y = proj( y, X ) + y ⊥ and max( ∥ y ⊥ ∥ , ∥ proj(y , X) ∥ ) < 1 , there exist -1 < α < 1 and 0 < β < 1 such that α 2 + β 2 = 1

By restricting on the subspace span { [1 , 0 , 0 , ..., 0] , [0 , 1 , 0 , ..., 0] } and letting Y = span { y } , we plot A Y, A x and A y in Figure 7. We also characterize the three sets S I , S II , S III , which have boundaries defined by A Y, A x and A y as shown in Figure 7:

One can verify that A a and A b are both in the set S II . Therefore, by denoting θ b = θ ( A x, A b ) and θ a = θ ( A x, A a ) , we have 0 < θ b , θ a < π

In the following, we only need to show that θ b > θ a . We have,

Since 1+ λ -λα > 0 , we consider two cases. For the first case, i.e., if 1 -λ -λα < = 0 , then θ b > θ a because tan θ b < 0 (i.e., θ b > π/ 2 ) or tan θ b is not well defined (i.e., θ b = π/ 2 ) while tan θ a > 0 (i.e., θ a < π/ 2 ). For the second case, i.e., if 1 -λ -λα < 0 , then θ b > θ a because tan θ b > tan θ a > 0 .

A.2 Details of Gradient in Eqn. 11

A.3 Experiments with different backbones

Currently, ResNet50 is the most widely used backbone 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 DukeMTMC-reID are provided on the repository on github.

The mAPs yielded with ResNet18 and ResNet34 are lower than those with ResNet50 due to the smaller structures of the former two networks. The performances with ViT-B/16 are slightly better than that with ResNet50 because of its stronger capacity. We do not adjust hyper-parameters 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 performance. 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 can be applied to any backbones without modification.

The 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.

A.4 Implementation details

We use the unlabeled training set of target domain to train the model, and use the 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 256 × 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 as 0.00035 and

decayed by 0.1 every 25 epochs. Model is totally trained for 75 epochs. Model is trained on a server with 4 RTX 4090 GPU, and 128G memory.

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, resulting 256 images. For inter-class constraint computation ( L inter ), we update the 16 memory banks corresponding to those sampled 16 identities by their closest memory bank vector (hard sampling strategy) via L inter .

Compared with the baseline model, our BMW method introduces two extra steps:

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.

Computing the update vector for the 16 memory banks by 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).

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).

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.

The dynamic weighting could capture the overall distribution because:

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.

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

VeRi-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 are summarized in Table 5.

It can be observed from Tabel 5 that 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 is even comparable with methods that use deeper backbone [DCC (ResNet50-ibn)], extra annotations [PPLR (part features + camera labels)] or stronger re-ranking PPLR (part features + camera labels + re-ranking)].

As we use the same hyper-parameters on the VeRi-776 dataset, i.e. , λ intra = 0 . 9 and λ inter = 0 . 2 , experiments on VeRi-776 further show that hyper-parameters selected on one dataset can be applied on other datasets with promising performance.

A.6 Balance between the intra-class and inter-class constraints

The two constraints are balanced with two loss weights, i.e. , λ intra for the intra-class constraint L intra , and λ inter for the inter-class constraint L inter . The values of λ intra and λ inter should be different due to their distinct roles:

The term L intra encourages memory banks to approach corresponding positive training samples, a larger λ intra enhances the intra-class constraint and encourages the memory banks to closely represent their positive samples while maintaining discriminative power in feature space.

In the meantime, since L inter pushes memory banks away from each other, a larger λ inter may push memory banks far away from corresponding positive samples.

Therefore, λ inter should be smaller than λ intra from the above analysis.

We also conduct the hyper-parameter analysis, which shows that:

The configuration ( λ intra = 0 . 9 , λ 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.

The superior performance does not totally rely on careful selection of hyperparameters. We notice that BMWconsistently achieves better results than others as long as λ intra sits in [0 . 6 , 1] and λ inter in [0 . 1 , 0 . 25] .

Consequently, the hyperparameters are easily tunable for superior performance across different datasets.

By the way, the superiority range of λ inter is smaller than that of λ intra . This is because L intra decreases after several training iterations even when using the hard sampling strategy, while L inter stays large because of the large number of identities. We can apply a threshold on L inter to ignore memory bank pairs that are already far away from each other. This strategy will enlarge the operating range of λ inter .

A.7 Limitations

A.7.1 Noisy data

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.

A.7.2 Cross-domain scenarios

Our experiments demonstrate both strong performance and consistent hyperparameter transferability across datasets. While current unsupervised person ReID methods typically train and test on the same dataset, crossdomain 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 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.

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, a 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:

ReID is different from classification: In the classification task, the training and test sets have the same set of categories. However, in ReID task, identities in the training set do not exist in 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 ReID is performed based on image feature similarities, feature distance relationship is more important than classification prediction.

Classifier is not effective to optimize features: The standard classifier uses an FC layer and a SoftMax layer to predict probabilities. The cross-entropy loss is commonly used to supervise the predicted probabilities. Classifier focuses on predicting correct labels, instead of 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 bank update is to enhance the feature distance optimization and the performance of ReID, rather than accurately predicting image labels. E.g. , although L θ increases from 0.439 to 3.033 in the epoch 1 on Market, mAP on test set increases from 12.9% to 25.6%.

Memory bank is more robust against label noise induced by unsupervised clustering:

A single noisy sample will perturb all parameters in FC layer: As an FC is followed by the SoftMax layer, each noisy sample will perturb all FC parameters via the gradient of the cross entropy loss, making FC parameters sensitive to label noise.

Only a few memory banks are perturbed by a single noisy sample: For a noisy sample in a training batch, only its corresponding memory bank is certainly to be perturbed. Other memory banks are perturbed only if the noisy label is selected as the hard sample, which has a very low probability of occurrence given the large number of identities in datasets. Thus, most memory banks will not be perturbed by a single noisy sample.

Therefore, memory banks are better for unsupervised ReID with stronger generalization capacity on test set.

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.

BMWis more effective at feature optimization:

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.

As conventional classifiers focus on accurate label prediction and do not directly optimize feature relationships, their abilities to optimize feature representations are limited.

Compared to conventional classifiers, the proposed BMW directly optimizes feature distance relationships with L θ , and memory banks are optimized with L M to facilitate the feature representation learning.

From the above analysis, the advantage of our BMW in feature representation learning over conventional classifiers is independent of data annotations. Thus, BMW is also more effective at 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 BMWand conventional classifiers is summarized in Table 6.

It can be observed from Table 6 that:

Compared to the conventional classifier, our BMW still enhances feature representations more effectively under the supervised setting.

Our unsupervised version of BMW is even comparable to the supervised circle loss method, which is a metric learning method. This shows the stronger feature optimization capacity of our BMW method.

Our BMW also outperforms other methods using memory bank by a clear margin. This further shows the superiority of our BMW method.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nA 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nSee the comments above in \"Weaknesses\" and \"Questions\"", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes, the paper briefly mentions in Sec. 6 BMW's sensitivity to noisy data and challenges in cross-domain transfer, but the discussion remains superficial.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

comments: 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.

STRENGTHS AND WEAKNESSES:
[Strengths]
- This paper is very well-written and easy to follow.
- 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.
- The proposed method is simple yet effective.
- Comprehensive experiment results demonstrating the proposed method are provided.

[Weaknesses]
- The experiments are conducted only with a ResNet50 backbone. Results with other backbones such as ViT would help validate the generality of the method.
- 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.
- 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).

QUESTIONS:
Please see the Weaknesses.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 4
ORIGINALITY: 4
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \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.\n\nQUESTIONS:\n1.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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}