Generalizable Person Re-identification via Balancing Alignment and Uniformity

Authors: yoonki  cho
jaeyoon kim woo
jae  kim
junsik  jung
sung-eui  yoon
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-15 12:55:48

Abstract: Domain generalizable person re-identification (DG re-ID) aims to learn discriminative representations that are robust to distributional shifts. While data augmentation is a straightforward solution to improve generalization, certain augmentations exhibit a polarized effect in this task, enhancing in-distribution performance while deteriorating out-of-distribution performance. In this paper, we investigate this phenomenon and reveal that it leads to sparse representation spaces with reduced uniformity. To address this issue, we propose a novel framework, Balancing Alignment and Uniformity (BAU), which effectively mitigates this effect by maintaining a balance between alignment and uniformity. Specifically, BAU incorporates alignment and uniformity losses applied to both original and augmented images and integrates a weighting strategy to assess the reliability of augmented samples, further improving the alignment loss. Additionally, we introduce a domain-specific uniformity loss that promotes uniformity within each source domain, thereby enhancing the learning of domain-invariant features. Extensive experimental results demonstrate that BAU effectively exploits the advantages of data augmentation, which previous studies could not fully utilize, and achieves state-of-the-art performance without requiring complex training procedures.

Main paper: 
 introduction: 
 Person re-identification (re-ID) aims to match a person with the same identity as a given query across disjoint camera views and different timestamps [88, 96] . Thanks to the discriminative features learned from deep neural networks, significant achievements have been made in this task [1, 44, 45, 72, 73, 78] . However, these learned feature spaces rely on the assumption of independent and identically distributed (i.i.d.) training and testing data, which leads to substantial performance degradation in unseen domains with distributional shifts. To address this issue, domain generalizable person re-ID (DG re-ID) has emerged, focusing on learning representations that are robust to domain shifts [71] .Existing DG re-ID methods often leverage advanced network architectures, such as feature normalization modules [34-36, 53, 107] , domain-specific designs [12, 86, 90] , and the integration of transformers [51, 60] . Alternatively, some approaches employ domain adversarial training [6, 20, 91] or meta-learning strategies [10, 40, 71, 94] to learn domain-invariant representations across source domains. Although these studies have shown promising results, they often involve complex training procedures that require significant engineering effort or are prone to training instability [3, 62, 66] .On the other hand, data augmentation is a straightforward solution to enhance generalization capability by simulating diverse data variations during training. Due to its simplicity and effectiveness, numerous efforts have been made to adopt this approach for various DG tasks [69, 75, 93, 99, 104, 105] . However, in the context of DG re-ID, some data augmentations have been observed to exhibit a polarized effect -improving performance in the source domain while potentially degrading it in the target domain. A notable example is Random Erasing [101] , a technique widely used in person re-ID, which has been shown to deteriorate cross-domain re-ID performance [34, 56, 94] . Despite this observation, the underlying causes and potential solutions for this phenomenon remain underexplored.In this paper, we first investigate the polarized effect of data augmentations in DG re-ID. Recent studies have shown that alignment and uniformity in the representation space are closely related to feature generalizability [21, 49, 57, 65, 80, 81] . Building upon this, we reveal that data augmentations can induce sparse representation spaces with less uniformity, which may be detrimental to the open-set nature of the re-ID task, where learning diverse visual information is crucial for generalization [7, 12, 58, 87] . Based on our analysis, we propose a simple yet effective framework, Balancing Alignment and Uniformity (BAU), which alleviates the polarized effect of data augmentations by maintaining a balance between alignment and uniformity. Specifically, it regularizes the representation space by applying alignment and uniformity losses to both original and augmented images. Additionally, we introduce a weighting strategy that considers the reliability of augmented samples to improve the alignment loss. We further propose a domain-specific uniformity loss to promote uniformity within each source domain, enhancing the learning of domain-invariant features. Consequently, BAU effectively exploits the advantages of data augmentation, which previous studies could not fully utilize, and achieves state-of-the-art performance on various benchmarks. In summary, our contributions are as follows:• We investigate the polarized effect of data augmentations in DG re-ID and reveal that they can lead to sparse representation spaces, which are detrimental to generalization. • We propose a novel BAU framework that mitigates the polarized effect of data augmentations by balancing alignment and uniformity in the representation space. Additionally, we introduce a domain-specific uniformity loss to enhance the learning of domain-invariant representations. • Through extensive experiments on various benchmarks and protocols, we demonstrate that BAU achieves state-of-the-art performance, even without complex training procedures.Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?Answer: [Yes] Justification: The claims are clearly stated in the abstract and the introduction.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. 
related work: 
 Generalizable Person Re-identification. Domain generalizable person re-identification (DG Re-ID) focuses on learning discriminative representations for person retrieval that are robust across unseen domains with distributional shifts. Significant efforts have been made in this task [2, 4, 17, 52, 60, 71, 82] due to its practicality, as it does not require additional model updates for target data, unlike domain adaptation approaches [19, 22, 23, 54, 102] . Given that learned feature statistics can be biased toward the source domain [5, 24, 47, 76] , various feature normalization methods [10, 27, 34, 35, 107] have been proposed to mitigate this domain bias. For instance, SNR [36] eliminates style bias through feature disentanglement, and GDNorm [53] refines feature statistics using a Gaussian process. To achieve domain-invariant representations, several studies [10, 61, 71, 90] leverage domain-adversarial training [20, 41] or meta-learning [18, 40] . DDAN [6] utilizes domain-wise adversarial feature learning to reduce domain discrepancies for domain invariance. M 3 L [94] employs meta-learning to simulate the train-test process of domain generalization with multi-source datasets. There have also been attempts [50, 51] that explore advanced matching strategies between query and gallery images for retrieval to improve interpretability and generalization performance. Recently, Mixture-of-Experts (MoE) based approaches [12, 33] have emerged, where multiple domain-specific experts are trained and applied to the target domain, with META [86] alleviating the model scalability issue by domain-specific batch normalization [5] . In contrast, we effectively utilize the diversity provided by data augmentations to enhance generalization without relying on advanced network architectures or encountering training instability associated with adversarial or meta-learning [3, 62, 66] .Alignment and Uniformity. Wang and Isola [81] proposed that contrastive learning encompasses two main objectives: alignment, which aims to learn similar representations for positive pairs, and uniformity, which strives to distribute representations uniformly on the unit hypersphere. This framework has significantly influenced representation learning by potentially indicating the feature generalizability [49, 57, 63, 65, 77, 80] . For instance, the concepts of alignment and uniformity have been extensively studied to learn robust representations for improved generalizability across various downstream tasks [21, 57, 77] or domains [65, 80] . This approach has also proven effective when applied to multiple data modalities, including images and text [49, 63] . Despite these advances, the potential of alignment and uniformity in addressing the challenge of DG re-ID remains largely unexplored. In this work, we address this gap by applying alignment and uniformity to person re-ID, balancing feature discriminability and generalizability to learn domain-invariant representations. 3 Method Problem Formulation. Given a set of K source domains,D S = {D k } K k=1 , each domain D k = {(x i , y i )} N ki=1 consists of images x i and corresponding identity labels y i , where N k denotes the number of images in a source domain D k . Using these source domains, we train a model f θ , parameterized by θ, to extract person representations f i = f θ (x i ) ∈ R d from the image x i , where d is the dimensionality of the representation space. The trained model is then evaluated on a target domain D T , which is unseen during training. While a general homogeneous DG task has a consistent label space across source and target domains within a closed-set setting, generalizable person re-ID is a heterogeneous DG problem, where each domain has a disjoint label space from the others [79, 103] . Consequently, it is an open-set retrieval task where trained models need to identify unseen classes.Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Justification: The paper includes the limitations in the appendix Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
polarized effect of data augmentation on in-and out-of-distribution: 
 Data augmentations, which apply random transformations to input data, are widely used across various tasks to improve training efficiency and model robustness [15, 39, 70, 89] . In the DG re-ID task, however, certain augmentations have shown a polarized effect: they enhance retrieval performance on in-distribution data while potentially degrading it on out-of-distribution data. For instance, Random Erasing [101] , which selectively erases pixels from parts of input images, has been shown to deteriorate cross-domain re-ID performance [56] . As a result, most DG re-ID methods [10, 12, 36, 86, 94] have simply discarded this technique despite its usefulness in standard re-ID settings. Nonetheless, the underlying phenomenon of this polarized effect remains underexplored.To investigate the polarized effect on re-ID performance between in-distribution (ID) and out-ofdistribution (OOD) scenarios, we conduct experiments1 using varying augmentation probabilities (i.e., the probability of applying data augmentation to input images). For data augmentations, we employ the widely used RandAugment [11] , known for its effectiveness across various vision tasks, which randomly applies transformations sampled from a predefined set, including comprehensive geometric manipulations and color variations. Considering the fine-grained domain characteristics of person re-ID, we exclude transformations that cause severe color distortion, such as Invert and Solarize, from the predefined set, and additionally utilize Random Erasing [101] . We train models with varying augmentation probabilities using a standard training pipeline that employs cross-entropy and batch-hard triplet loss [30, 56] . Following the existing DG re-ID protocol [86] , the training set of MSMT17 (MS) [83] , CUHK03 (C3) [44] , and CUHK-SYSU (CS) [85] are used for model training as source domains, and Market-1501 (M) [95] as the target domain.Fig. 1a compares the performance on the Market-1501 dataset of two models: one trained on the same dataset (ID) and the other trained on MS+CS+C3 (OOD). While data augmentation improves ID performance, OOD performance consistently deteriorates as the augmentation probability increases. This discrepancy highlights the polarized effect of data augmentations in the open-set nature of person re-ID. In closed-set recognition tasks, strong class invariance learned through augmentations can enhance generalization. However, in open-set retrieval tasks, where the model needs to handle unseen classes, learning diverse visual information becomes more crucial for generalization [58, 87] . Although augmentations improve model robustness within training distributions, they can also lead the model to focus on dominant visual information that is easily invariant to augmentations, as shown in Fig. 2 . Consequently, this can result in sparse representation spaces, where the model focuses on learning dominant features while neglecting subtle cues that can be generalized to other domains. To further explore the effect of data augmentations on the representation space, we leverage the concepts of alignment and uniformity [81] , which are key properties of feature distributions on the unit hypersphere.Alignment is defined as the expected distance between positive pairs:EQUATIONwhere P pos is the distribution of positive pairs. Uniformity, on the other hand, is defined by the logarithm of average pairwise Gaussian potential:EQUATIONwhere P data is the distribution of given data. These two properties reflect that positive pairs are close to each other (alignment) while the overall distribution of embeddings is uniformly spread on the hypersphere (uniformity). Several studies have demonstrated that both are essential for generalization, ensuring that the representation space achieves feature discriminability while preserving maximal information from the data [21, 65, 80] .Fig. 1b illustrates the alignment and uniformity of representations in OOD scenarios (Market-1501 for MS+CS+C3 → Market-1501) for models trained with varying augmentation probabilities. As depicted, the use of augmentations leads to more alignment, thereby enhancing intra-class invariance compared to models trained without augmentation. Conversely, in terms of uniformity, higher augmentation probabilities result in less uniform embeddings, indicating that the model fails to sufficiently preserve the diverse information from the data. However, to achieve generalizability in the open-set re-ID task, learning diverse information (i.e., more uniformity) is crucial [7, 12, 58] . Furthermore, as shown in Fig. 1c , the feature embeddings become increasingly less uniform with higher probabilities, and this becomes more evident as distributional shifts occur, as indicated by the red plot in the same figure. It suggests that simple training with data augmentations causes the model to become dominated by specific in-distribution data and fail to learn diverse visual cues, leading to degraded generalization performance with a sparse representation space with less uniformity.Our analysis highlights the polarized effect of data augmentations in person re-ID, showing that while they enhance in-distribution performance, they can deteriorate out-of-distribution performance by leading to a sparse representation space. Nevertheless, data augmentations remain a promising technique to improve generalization by increasing both the diversity and robustness of training data.In the following subsection, we present a method to mitigate this effect by incorporating alignment and uniformity, using both original and augmented images to balance feature discriminability and generalizability.We introduce a simple yet effective framework, Balancing Alignment and Uniformity (BAU), which mitigates the polarized effect of data augmentations by maintaining a balance between alignment and uniformity. The overview is illustrated in Fig. 3 . Given an input batch, we generate augmented views of the images, x = t(x), where t ∼ T denotes augmentations sampled from the distribution T . Our model then extracts features from both the original and augmented images, denoted as f i = f θ (x i ) and fi = f θ (x i ), respectively. Since simple training with augmented images can lead to polarized effects (Sec. 3.1), we apply both alignment and uniformity losses to the features of the augmented images to achieve both feature discriminability and generalizability simultaneously. Specifically, the alignment loss enhances feature discriminability by promoting invariance to diverse augmentations, while the uniformity loss encourages generalizability by striving for a uniform distribution of features on the hypersphere, thereby preserving diverse visual information from the data. Alignment Loss. We reformulate the alignment loss to minimize the expected feature distance of positive pairs between original and augmented images, defined as:EQUATIONwhere I pos = {(i, j) | y i = y j } is the index set for the positive pairs within a mini-batch, and |•| denotes the cardinality of the set. With alignment loss, the model can learn invariance to various transformations introduced by data augmentations, thereby enhancing feature discriminability. However, some augmented samples may suffer from significant corruption due to aggressive augmentations, and learning invariance with these samples can potentially degrade the training process.To address this issue, we introduce a weighting strategy for the alignment loss that considers the reliability of augmented samples. Based on studies that handle noisy samples by leveraging the relationship with nearest neighbors [8, 9, 32, 84, 100] , we compute the weight as the Jaccard similarity of k-reciprocal nearest neighbors between the augmented sample and the original sample, defined by:EQUATIONwhere R k (f i ) is the set of indices for k-reciprocal nearest neighbors within a mini-batch of the feature f i . Intuitively, a low weight implies that the augmented feature fi and the original feature f j are not strongly correlated, indicating that learning invariance between them can provide unreliable information to each other. We reformulate the alignment loss in Eq. ( 3) with weights w, computed as:EQUATIONwhere w = w (i,j)∈Ipos wij is the normalized weight. This strategy allows the alignment loss to focus on reliable pairwise relationships between the original and augmented images without noisy samples.Uniformity Loss. Following Wang and Isola [81] , we compute the uniformity loss to balance feature discriminability and generalizability as:EQUATIONwhere I data = {(i, j) | i ̸ = j} is the index set of all distinct pairs within a mini-batch. By incorporating the uniformity loss, the learned representations can be uniformly distributed on the hypersphere, maintaining the diversity of the feature space. This feature diversity is crucial for the generalization capability, as it prevents the model from overfitting to sparse dominant representations and encourages learning subtle cues that can be generalized across different domains.Domain-specific Uniformity Loss. While the uniformity loss within a mini-batch can improve the diversity of the feature space, batched samples may not fully capture the global structure of the entire representation space. Additionally, high uniformity alone does not guarantee domain invariance, since features from the same domain may still cluster together, as illustrated in Fig. 3 (c). To address these issues, we employ a feature memory bank, M = {c 1 , ..., c N } ∈ R N ×d with class prototypes c ∈ R d , where N is the number of classes in the given datasets. Each class prototype represents the feature vector for its respective class and is updated continuously during training using the momentum strategy [23, 28] as:EQUATION)where f is the extracted features of original images with the same class label as c, and µ ∈ [0, 1] is the momentum coefficient. To mitigate the inherent domain bias in simple uniformity, we additionally apply a domain-specific uniformity loss, which enhances the uniformity between the features and prototypes within their corresponding domain as follows:EQUATIONwhere N (f ) is the index set of nearest prototypes of f that are from the same source domain but different class, and N is the number of nearest prototypes, which is set to match the size of the minibatch. This loss attempts to uniformly distribute the features of each domain, reducing domain bias for domain-invariant representation space. Furthermore, by using a memory bank to compute uniformity with nearest prototypes, we can efficiently consider the overall structure of the representation space.Overall Training Objective. The overall loss function of BAU is then given by:EQUATIONwhere L ce and L tri are the cross-entropy loss and triplet loss, respectively, applied only to the original images. Here, λ is the weighting parameter for the alignment loss. By ensuring alignment and uniformity with both original and augmented images, BAU effectively mitigates the detrimental effect of data augmentations while simultaneously exploiting the diversity they introduce. Moreover, BAU is a simple yet effective framework that directly regularizes the representation space without the need for advanced network architectures or complex training procedures. 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datasets and evaluation protocols: 
 M+MS+CS C3 M+CS+C3 MS MS+CS+C3 M Protocol-3 Full-(M+MS+CS) C3 Full-(M+CS+C3) MS Full-(MS+CS+C3) MWe conduct experiments using the following datasets: Market-1501 [95] , MSMT17 [83] , CUHK02 [43] , CUHK03 [44] , CUHK-SYSU [85] , PRID [31] , GRID [55] , VIPeR [26] , and iLIDs [97] , with dataset statistics shown in Table 1 . For simplicity, we denote Market-1501, MSMT17, CUHK02, CUHK03, and CUHK-SYSU as M, MS, C2, C3, and CS, respectively. Evaluation metrics include mean average precision (mAP) and cumulative matching characteristic (CMC) at Rank-1.Following previous studies [71, 86, 90, 94] , we evaluate the proposed method across three protocols, as detailed in Table 2 . For Protocol-1, we utilize all images from M, C2, C3, and CS, including both training and testing data, for model training. We then evaluate the model on four small-scale re-ID datasets, specifically PRID, GRID, VIPeR, and iLIDs. The final performance on these small-scale datasets is obtained by averaging the results of 10 repeated random splits of the query and gallery sets. For Protocol-2 and Protocol-3, we follow a leave-one-out evaluation setting with four large-scale datasets: M, MS, C3, and CS. Three datasets are used as the source domain, and the remaining one is used as the target domain. Protocol-2 uses only the training data from the source domains for model training, whereas Protocol-3 utilizes both the training and testing data from the source domains. Since CUHK-SYSU (CS) is a person search dataset with a single camera view, it is only used for training.Following previous studies [34, 50, 51, 86, 90] , we use ResNet-50 [29] pre-trained on ImageNet [13] with instance normalization layers as our backbone. All images are resized to 256 × 128. For each iteration, we sample 256 images, consisting of 64 identities with 4 instances for each identity. The total batch size during training is 512, including both original and augmented images. Random flipping, cropping, erasing [101] , RandAugment [11] , and color jitter are used for data augmentation. We train the model for 60 epochs using Adam [38] with a weight decay of 5 × 10 -4 . The initial learning rate is set to 3.5 × 10 -4 and is decreased by a factor of 10 at the 30th and 50th epochs. A warmup strategy is applied during the first 10 epochs. The momentum µ is set to 0.1. We empirically set the weighting parameter λ to 1.5 and k for the weighting strategy to 10. We implement our framework in PyTorch [64] and utilize two RTX-3090 GPUs for training.We compare our method with state-of-the-art DG re-ID methods on Protocol-1, with the results presented in Table 3 . We also report the results of previous studies that use DukeMTMC-reID [98] for training, denoted as D in the table, while we exclude this dataset from training since it has been 4 . The results demonstrate that our method outperforms other methods, confirming the generalization capability of BAU on large-scale datasets. Specifically, we achieve higher average mAP scores across three datasets than the previous state-of-the-art ACL [90] , with improvements of +2.7% and +3.4% on protocol-2 and protocol-3, respectively.It is also noteworthy that our method achieves state-of-the-art performance without employing advanced feature normalization modules [10, 35, 36, 48, 53] or domain-specific network architectures [12, 86, 90] . Specifically, BAU is a simple yet effective framework that regularizes the representation space by ensuring alignment and uniformity between original and augmented images, without relying on domain-adversarial [6] training or meta-learning [4, 94] strategies.To evaluate the effectiveness of each component in BAU and validate our design choices, we conduct extensive ablation studies and analyses on Protocol-3, the most scalable setting in our experiments.Ablation study of loss functions for augmented images. We first analyze the impact of applying different loss functions to the augmented images, as shown in Table 5 . The baseline model, trained using cross-entropy and triplet losses without any augmented images (when p = 0 of Sec. 3.1), serves as a reference point. Applying only the cross-entropy loss L ce to the augmented images leads to a performance drop, aligning with our analysis in Sec. 3.1 that naive training with augmented images can degrade generalization performance. This result also aligns with studies demonstrating that crossentropy loss can easily overfit to biases and noise in the data [25, 59, 92] . When applying the proposed alignment loss L align , the performance improves, surpassing the baseline. Further incorporating the uniformity loss L uniform significantly boosts the performance, confirming the importance of balancing both alignment and uniformity to achieve better generalization. Finally, integrating the domain-specific uniformity loss L domain achieves the best performance, with notable improvements of +10.2%/+8.0% in average mAP/Rank-1 over the baseline. This validates that mitigating domain bias by uniformly distributing features within each domain is effective. In summary, these results highlight that while simply training with augmentations can be detrimental, carefully regularizing the representation space through alignment and uniformity allows the model to benefit from the augmented data, leading to improved generalization.Ablation study of weighting strategy and domain-specific uniformity loss. We conduct an ablation study to validate the effectiveness of the proposed weighting strategy for the alignment loss and the domain-specific uniformity loss, as shown in Table 6 . Without the weighting strategy in Eq. 3, the performance drops compared to the full BAU model, demonstrating the benefit of focusing on reliable pairs between original and augmented images to learn robust features. To investigate the importance of the domain-specific uniformity loss, we apply a simple uniformity loss with a feature memory bank without considering each domain (i.e., modifying Eq. ( 8) with nearest prototypes in the entire domain, not intra-domain). The performance decreases, highlighting the effectiveness of promoting uniformity within each domain for learning domain-invariant features. When both the weighting strategy and domain-specific prototypes are removed, the performance drops further, showing that each component provides complementary benefits, and integrating both in the BAU framework results in better generalization. This analysis validates the effectiveness of the proposed techniques, enabling BAU to achieve strong performance in the DG re-ID task by focusing on reliable pairs in alignment and promoting uniformity within each domain.Analysis of alignment and uniformity. To further validate the effect of our proposed method on the learned representation space, we analyze the alignment and uniformity properties of the baseline and BAU. Fig. 4a illustrates these properties for the representations on the testing data of Market-1501 when MS+CS+C3 → M under Protocol-3. When data augmentations are applied, the representations of the baseline become less uniform, resulting in decreased generalization performance, as discussed in Sec. 3.1. In contrast, BAU consistently maintains better alignment and uniformity compared to the baseline across all augmentation probabilities. Notably, even when data augmentation is not applied (i.e., p = 0), BAU still outperforms the baseline. This demonstrates that in addition to preventing the polarizing effect of data augmentation, alignment and uniformity are important factors for generalization, aligning with recent studies [21, 65, 80] . In summary, by explicitly regularizing the representation space with both original and augmented images, BAU achieves better generalization performance by balancing feature discriminability and generalizability, confirming that it effectively leverages the diversity from data augmentations.Analysis of weighting strategy and domain-specific uniformity loss. To explore the effect of the domain-specific uniformity loss, we visualize the t-SNE [74] plot of the training data when MS+CS+C3 → M under Protocol-3, with and without the domain-specific uniformity loss L domain . As shown in Fig. 4b , without L domain , the learned features exhibit domain-specific clusters, indicating a lack of domain invariance. On the other hand, applying L domain results in a more uniform distribution of features across domains, as evidenced by the increased uniformity values for each domain (reported in parentheses). This demonstrates the effectiveness of promoting uniformity within each domain to learn domain-invariant representations.To further analyze the proposed weighting strategy for the alignment loss, we conduct quantitative comparisons across varying augmentation probabilities, with and without the weighting strategy. As shown in Fig. 5a , our weighting strategy consistently improves performance across different augmentation probabilities, with the gap becoming more pronounced at higher probabilities where severe corruption from augmentations is more likely. Specifically, at an augmentation probability of 0.5, the weighting strategy improves the mAP from 78.3% to 79.5%, and at a probability of 1.0, the improvement is even more substantial, from 66.1% to 76.1%. These results demonstrate that BAU effectively enables learning invariance with reliable augmented samples, thanks to the proposed weighting strategy, which focuses on reliable pairs between original and augmented images.For qualitative analysis, Fig. 5b illustrates the weight scores w in Eq. ( 4) for the alignment loss. The top row shows the original images, while the bottom row represents their augmented versions with corresponding weight scores. As w progressively increases from left to right, the augmented images exhibit less distortion, indicating more reliable samples with informative augmentations. The weighting strategy allows the alignment loss to focus on augmented samples that are semantically similar to the original images, facilitating invariance learning from informative augmentations. 
discussion: 
 Although our method has shown promising results, it still has limitations to overcome. As a straightforward approach primarily based on data augmentations for given input data, our method could face challenges under very large domain shifts. While we adopt standard image-level augmentations, we do not incorporate more advanced techniques such as adversarial data augmentations [69, 75, 99, 104] or feature-level augmentations [37, 46, 106] , both of which have shown promise for generalization. Integrating these techniques with the proposed BAU framework could potentially further enhance generalization performance and would be an interesting direction for future research. Despite these limitations, our work is the first to thoroughly investigate and address the polarized effect of data augmentations in DG re-ID. It demonstrates the significant potential of balancing alignment and uniformity to improve generalization in person re-identification. We believe that our findings could inspire further research aimed at advancing generalization capabilities in this field. 
conclusion: 
 In this paper, we investigated the polarized effect of data augmentations in domain generalizable person re-identification. Our findings revealed that while augmentations enhance in-distribution performance, they can also lead to sparse representation spaces and deteriorate out-of-distribution performance. To address this issue, we proposed a simple yet effective framework, Balancing Alignment and Uniformity (BAU), which regularizes the representation space by maintaining a balance between alignment and uniformity. Comprehensive experiments on various benchmarks and protocols demonstrated that BAU achieves state-of-the-art performance without requiring advanced network architectures or complex training procedures. Furthermore, our extensive ablation studies and analyses validated the effectiveness of each component in BAU, emphasizing the importance of balancing alignment and uniformity to achieve robust generalization in person re-identification. 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?Answer: [NA] Justification: The paper poses no such risks.Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?Answer: [Yes]Justification: The paper properly mentions existing assets with citations.Guidelines:• The answer NA means that the paper does not use existing assets.• The authors should cite the original paper that produced the code package or dataset.• The authors should state which version of the asset is used and, if possible, include a URL. • The name of the license (e.g., CC-BY 4.0) should be included for each asset.• For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided. • If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset. • For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided. 
 
 Appendix: This section provides additional implementation details for the experiments in Sec. 3.1. RandAugment [11] includes comprehensive geometric manipulations and color variations2 : 'AutoContrast', 'Equalize', 'Invert', 'Rotate', 'Posterize', 'Solarize', 'Color', 'Contrast', 'Brightness', 'Sharpness', 'ShearX', 'ShearY', 'TranslateX', 'TranslateY', 'Cutout', 'SolarizeAdd'. Since person re-identification is a fine-grained task that requires distinguishing individuals with subtle distinctions, severe color distortion can deteriorate feature discriminability. Therefore, we exclude 'Invert', 'Posterize', 'Solarize', and 'SolarizeAdd' from the predefined set of RandAugment and additionally utilize Random Erasing. Following the conventional training pipeline [56] , we configured the in-distribution model training with a batch size of 64, consisting of 16 identities with 4 instances for each identity. We train the model for 120 epochs using Adam [38] optimizer with weight decay of 5 × 10 -4 . The initial learning rate is set to 3.5 × 10 -4 and is decreased by a factor of 10 at the 40th and 70th epochs. A warmup strategy is also applied during the first 10 epochs. For the out-of-distribution model training, we sample 256 images, consisting of 64 identities with 4 instances for each identity. Other settings are the same as the implementation details in the main paper.ViT-B/16 MobileNetV2 To further explore the commonality of the polarized effect in data augmentation for DG re-ID, we investigate its presence across different backbones, loss functions, and augmentation types.Backbone and loss function. We examine the polarized effect across different backbone architectures, including transformer-based (ViT-B/16 [16] ) and lightweight (MobileNetV2 [67] ) networks. Following the same experimental protocol outlined in Sec. 3.1, we train these models using crossentropy and batch-hard triplet loss [30] , with and without data augmentations (i.e., Random Erasing [101] and RandAugment [11] ). As shown in Fig 6a , the polarized effect is consistently observed across different backbones. Additionally, we investigate this effect across different loss functions on a ResNet-50 backbone, including ArcFace [14] and PCL [23, 42] , which are widely used for re-ID and retrieval tasks. As shown in Fig. 6b , the polarized effect persists across all loss functions, and these results confirm that the polarized effect is not limited to specific architectures and loss functions.Augmentation. We investigate the existence of the polarized effect of individual augmentations used in BAU, specifically Random Erasing, RandAugment, and Color Jitter. Following the experimental setting outlined in Sec. 3.1, we conduct experiments using a ResNet-50 backbone with cross-entropy and batch-hard triplet loss. As shown in Fig. 6c , the results indicate that the performance drop in the unseen domain is primarily driven by the polarized effects observed in Random Erasing and RandAugment, while Color Jitter does not exhibit this behavior, consistent with previous findings in the field. While Random Erasing and RandAugment can introduce significant distortions (e.g., pixel drops) to images, Color Jitter only provides simpler color distortions, which could enhance model robustness to variations in lighting and color conditions in unseen environments. Additionally, as shown in Fig. 7 , increasing the augmentation probability of Random Erasing and RandAugment further degrades performance and reduces uniformity, confirming the polarized effect caused by these augmentations in DG Re-ID.In summary, we consistently observe the polarized effect across different backbones, loss functions, and augmentation types, suggesting that this phenomenon is general in DG re-ID. Furthermore, the proposed BAU consistently improves various baselines with different backbones and loss functions (Table . 7) and various augmentation types (Table . 8).To further validate the versatility and effectiveness of the proposed BAU framework, we conduct experiments across different backbone architectures and loss functions.We first apply BAU to two distinct types of backbones: MobileNetV2 [67] , a lightweight network, and ViT-B/16 [16] , a transformer-based architecture. For training, we use cross-entropy and batch-hard triplet loss [30] with Random Erasing [101] and RandAugment [11] for augmentations. As shown in Table . 7, BAU consistently improves the performance of both baseline models across different backbones. For example, with MobileNetV2, BAU enhances the average mAP from 26.1% to 33.2% and Rank-1 from 38.0% to 45.7%, representing significant improvements. These results demonstrate the broad applicability of BAU across different backbones, suggesting that the proposed framework is effective regardless of underlying architecture.We further evaluate the effectiveness of BAU on models trained with different loss functions, Arc-Face [14] and PCL [23, 42] . For training, we employ a ResNet-50 backbone with Random Erasing and RandAugment for augmentations. As shown in Table . 7, BAU consistently improves the performance of both baselines across different loss functions. For instance, with ArcFace, BAU increases the average mAP from 36.8% to 44.6% and Rank-1 from 50.3% to 59.3%, delivering substantial gains. These results confirm that BAU is not limited to any specific loss function and can be seamlessly integrated into various re-ID loss formulations to enhance generalization capabilities.In summary, these results validate the effectiveness and versatility of BAU, showing that it can be applied across diverse architectures and loss functions to improve generalization in DG re-ID. Notably, BAU achieves these improvements as a simple regularization technique, without requiring complex training procedures or additional trainable parameters. This highlights its efficiency and potential for easy integration into various baseline models in the DG re-ID task.Table 8 presents an ablation study on the impact of different data augmentations on the generalization performance of our method under Protocol-3. We investigate three augmentations used in the proposed method: Random Erasing [101] , RandAugment [11] , and Color Jitter. As shown in the table, the results demonstrate that each augmentation technique improves the generalization performance, indicating that our method successfully mitigates the polarized effect of data augmentations. Specifically, even though Random Erasing has generally been shown to degrade generalization performance in previous studies, our method can effectively exploit the advantages of this augmentation technique. Furthermore, it is worth noting that even without any augmentations, the proposed method still outperforms the baseline (see Table 5 in the main paper), highlighting the effectiveness of balancing alignment and uniformity in improving generalization. When all three augmentations are applied together, we achieve the best performance, with an average mAP of 52.3% and Rank-1 accuracy of 65.7%. This highlights that the diversity of data augmentations enables the proposed method to learn more diverse information from the data, thereby enhancing feature generalizability.To further investigate the impact of data augmentation on our method, we conduct experiments by varying the probability p of applying data augmentations during training. As shown in Table 9 , increasing the augmentation probability generally leads to better generalization performance up to a certain point. Setting p to 0.5 yields the best results, so we empirically set this configuration. Further increasing the probability to 0.75 or 1.0 leads to a slight decrease in performance, indicating that excessive overlap of the three augmentations may introduce noise and hinder the learning process.In summary, these experimental results validate the effectiveness of data augmentations in enhancing the generalization capability for person re-identification -a potential that previous studies have not fully explored. The proposed BAU framework enables the model to leverage the diversity introduced by augmentations, resulting in more robust and generalizable representations for unseen domains.We conduct a parameter analysis to investigate the impact of the weighting strategy and the alignment loss on the generalization performance of our proposed method. Specifically, we evaluate the effect of varying the number of k-reciprocal nearest neighbors k for the weighting strategy and the weighting parameter λ for the alignment loss on the MS+C3+CS → M setting under Protocol-3. Figure 8a shows the mAP and Rank-1 accuracy of our method with different values of k for the weighting strategy. As k increases, the performance initially improves, reaching the best results at k = 10 with an mAP of 79.5% and a Rank-1 accuracy of 91.1%. However, when k is set to 5, the performance slightly lags behind the baseline (see Table 5 in the main paper). This result implies that the weighting strategy is not effective if unreliable weights are used. Setting k to 5 is too strict for computing reliable weights, as we observed that it causes the scores of too many samples to be close to zero, leading to insufficient learning of alignment. Figure 8b presents the mAP and Rank-1 accuracy of our method with different values of λ for the alignment loss. We observe that setting λ to 1.5 yields the best performance. This demonstrates the importance of balancing the contribution of the alignment loss with other loss terms. When λ is too small, the alignment loss may not sufficiently enforce feature invariance to augmentations. On the other hand, a large λ may overemphasize alignment and hinder uniformity, leading to the learning of less diverse information.In summary, these results highlight the importance of a reliable weighting strategy and achieving a balance between alignment and uniformity. The optimal values of k = 10 and λ = 1.5 are used in all our experiments reported in the main paper.The insights behind our method, balancing alignment and uniformity to induce feature diversity, could be valuable for improving generalization in other fine-grained and open-set retrieval tasks. By extending this approach to related domains, such as vehicle re-identification or face recognition, the robustness and real-world applicability of various computer vision systems could be enhanced. Furthermore, by learning more generalizable domain-invariant features, the proposed method could mitigate biases (e.g., race, gender) in person re-identification systems by focusing on domain-agnostic visual cues rather than spurious correlations. Further research specifically evaluating fairness impacts would be valuable to verify and quantify this potential benefit.However, person re-identification techniques can potentially have negative impacts, such as the infringement of privacy due to the abuse of surveillance systems. For instance, these methods can raise privacy concerns, as individuals may be tracked without consent as they move through public spaces. Researchers and users of person re-identification technology should be attentive to using it in an appropriate manner while considering ethical issues. Particular care should be taken to avoid using datasets with known ethical concerns for research. The development and deployment of person re-identification systems should be accompanied by appropriate safeguards and regulations to prevent misuse and protect individual privacy rights.NeurIPS Paper Checklist38th Conference on Neural Information Processing Systems (NeurIPS 2024).More details are provided in the appendix.https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet 


Decision: This paper investigates the polarized effects of data augmentations in DG re-ID and reveal that they can lead to sparse representation spaces, which are detrimental to generalization. To address it, they propose a framework BAU that mitigates the polarized effects of data augmentations by balancing alignment and uniformity in the representation space, where a domain-specific uniformity loss is introduced to enhance the learning of domain-invariant representations.

Reviewers have appreciation for writing/presentation, sufficient experiments, clear motivation, simple and effective framework, while raising concerns on relation with contrastive loss (Reviewer Ywj9), validation on more baselines (Reviewer L6wV), limited innovations (Reviewer fzqs, 4LRb: alignment and uniform concepts were already introduced in previous work), polarized phenomenon on augmentation types (Reviewer 4LRb). 

In considering the insight on the polarized effects caused by data augmentations in DG re-ID and the simple yet effective framework design,  AC arrives at an acceptance recommendation. AC highly encourages the authors to incorporate the suggestions of reviewers, add the analysis on different augmentation types, and clarify the contribution of this work in the revision.

Reviews: review: paper_summary: paper_summary: The authors investigate the polarized effects of data augmentations in DG re-ID and reveal that they can lead to sparse representation spaces, which are detrimental to generalization. To address it, they propose a novel BAU framework that mitigates the polarized effects of data augmentations by balancing alignment and uniformity in the representation space. And then they further introduce a domain-specific uniformity loss to enhance the learning of domain-invariant representations.
strength_weakness: strength_weakness: The effects of the BAU framework are validated on one baseline model in Table 5, it will be more convincing if it is validated on three typical baseline models such as supervised, self-supervised and unsupervised models.
questions: questions: 1.Whether different alignment strategies will affected the effect of the proposed framework?
2.The effects of the BAU framework are validated on one baseline model in Table 5, it will be more convincing if it is validated on three typical baseline models such as supervised, self-supervised and unsupervised models.
3.How about the versatility of the BAU framework? Do these losses apply to the three typical models？
limitations: limitations: The authors have given the limitations of the proposed model, the authors mentioned that, as a straightforward approach primarily based on data augmentations for given input data, the method could face challenges under very large domain shifts. And they adopt standard image-level data augmentations, such as random flipping, erasing, and color jitter, and do not incorporate more advanced augmentation techniques, such as adversarial data augmentations or feature-level augmentations, which have also shown to be promising for domain generalization.
paper_summary: paper_summary: Although data augmentation can improve in-distribution performance, it may lead to a sparse representation space, thereby reducing out-of-distribution performance.
To address this issue, the authors proposed a simple yet effective framework, Balancing Alignment and Uniformity (BAU), which effectively regularizes the representation space by maintaining balance between alignment and uniformity. 
BAU achieves state-of-the-art performance on various benchmarks and protocols, and extensive ablation studies validated the effectiveness of each component in BAU.
strength_weakness: strength_weakness: 1.As stated in [1], alignment and uniformity are two key properties related to the contrastive loss. 
Can the polarized effects of data augmentation in DG re-ID be resolved by applying only the contrastive loss? 

2.For Alignment Loss, how much does the sample that is corrupted during augmentation affect performance? This seems to be something that can be ignored.

3.The pipeline in this paper is very similar to Weak-Strong Augmentation[2], and the author needs to discuss the difference between them. 

[1]Wang, Tongzhou, and Phillip Isola. "Understanding contrastive representation learning through alignment and uniformity on the hypersphere." International conference on machine learning. PMLR, 2020.

[2]Li, Yu-Jhe, et al. "Cross-domain adaptive teacher for object detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2022.
questions: questions: Same to the Weaknesses.
limitations: limitations: The authors have addressed the limitations and potential negative societal impact of work.
paper_summary: paper_summary: This paper investigates the polar effects of data augmentation in the domain of generalizable person re-identification. To address the problem of augmented data degrading out-of-distribution performance, this paper proposes a Balanced Alignment and Uniformity (BAU) framework, which normalizes the representation space by maintaining a balance between alignment and uniformity.
strength_weakness: strength_weakness: Alignment and uniformity in the author's paper are not well explained and are concepts that have been applied from other articles, this paper needs to explain the meaning of these two terms in the context of the ReID task scenario as well as the need to elaborate on the differences and connections between this paper and the source of the ideas.

The trends shown in Figure 1c of this paper are consistent across the three different domains as the proportion of data augmentation increases, but what is shown in Figure 1a is the probability of being able to both increase and decrease. The results presented by the authors are not sufficient to support their conclusions.

Is there any relevant experimental or theoretical support for this paper's claim that current generalization methods affect training stability?

This paper lacks a separate analysis of the impact of different kinds of data augmentation, and according to my previous research, adding random erasing directly reduces the generalization performance of the model.

This paper lacks experimental comparisons with more recent methods and only compares methods from 2022 and earlier.
questions: questions: See the weakness.
limitations: limitations: The paper is not clearly written, the authors do not present their contributions well, some of the experimental results in the paper do not support their conclusions, and there is a lack of comparison with more recent methods.
paper_summary: paper_summary: This paper investigates the side effects of data augmentation in domain generalizable person ReID problem and proposes a framework for mitigating the negative effects. It is found that the data augmentation enhances the performance of a ReID model on its training domain, while degrading the performance of it on unseen domains. To alleviate it, a simple framework, Balancing Alignment and Uniformity (BAU), is proposed to maintain a balance between alignment and uniformity in the latent space, along with a domain-specific uniformity loss for domain-invariant representation. The empricial results show the effectiveness of the proposed methods on various benchmarks.
strength_weakness: strength_weakness: # Major concerns
In short, I have concerns with regard to the generalization of the polarized effects by the data augmentations, and a more thorough investigation is needed in terms of the types of the backbone, loss function, and data augmentation method.
- Is the performance degradation on unseen domains due to the data augmentations generalized, regardless of model architecture and loss function? In this paper, the experiments are limited to a specific backbone and loss functions (i.e., ResNet50 trained with cross entropy and triplet losses). Would a similar polarized phenomenon appear in other backbones (e.g., ViT) or other loss functions? In this regard, while the paper mentions that the random erasing (RE) augmentation causes performance degradation on unseen domains, QAConv [2] reports performance improvement due to RE in cross-domain evaluation (Table 1 in appendix of [2]).
- Wouldn’t the polarized effect tend to be different depending on the type of data augmentation? The individual analysis on the effect of each augmentation is required. In this regard, RandAugment and Color Jitter augmentations are used by many domain generalizable ReID methods. Are they relatively safe and is there any basis for this? If so, the performance decrease on the unseen domain in Figure 1 is seen as mainly due to RE.

# Minor concerns
- I'm a little concerned about whether the comparison of the methods in Tables 3 and 4 are fair. It seems that BAU uses more abundant augmentations. Are there any significant differences in augmentations used for each method? Also, it might be necessary to check whether the polarized effect occurs when the the data augmentations used in BAU are applied to other DG methods.
- The novelty of the proposed method is somewhat limtied, since it largely depends on the alignment and uniformity loss proposed in [1]. However, the weighting strategy for alignment loss and the domain-specific uniformity loss are newly introduced, and their effectivenss are demonstrated in ablation study.
- The analysis on the increase of training cost is required. For example, the weighting strategy for alignment loss requires additional computation of Jaccard similarity of k-reciprocal nearest neighbors within a mini-batch. Is this negligible?

[1] Wang and Isola, Understanding contrastive representation learning through alignment and uniformity on the hypersphere, ICML, 2020.  
[2] Liao and Shao, Interpretable and Generalizable Person Re-identification with Query-Adaptive Convolution and Temporal Lifting, ECCV, 2020.
questions: questions: - In Equation 5, the distance between $\tilde{f_i}$ and $f_j$ is computed. How about computing distance between $\tilde{f_i}$ and $f_i$ or between $f_i$ and $f_j$?
- In Equation 8, I understood that $f_i$ can be pushed against its class prototype. However, to push against only the class prototypes that $f_i$ does not belong to would be more intuitive.
limitations: limitations: The limitations on experiments for very large domain shifts and more advanced augmentation methods are discussed.

Comments: title: A gentle reminder for reviewer-author discussion
A gentle reminder for reviewer-author discussion
A gentle reminder for reviewer-author discussion
A gentle reminder for reviewer-author discussion
comment: Dear reviewers and chairs,

We sincerely thank all reviewers for their responses and efforts during the discussion period.
Your valuable comments have significantly contributed to improving our work.
We are pleased that our responses have addressed concerns and received positive feedback with increased ratings, which has been very encouraging for us.

Given the short remaining time of the discussion period, we would like to kindly ask if there are any remaining questions or concerns. 
We would be glad to address them with additional clarifications or results as soon as possible.

Lastly, we would like to emphasize some final points and the key contributions of our work:

1. **Insightful investigation of polarized effects in DG re-ID**: Our work is the first to thoroughly investigate and address the critical issue of polarized effects caused by data augmentations in DG re-ID. This phenomenon has been overlooked in previous studies, and our comprehensive analysis provides new insights into the field.
***
> **Note**: We have confirmed the commonality of our analysis and insights behind the polarized effect across different backbones, loss functions, and augmentation types in Figures A and B. We will include these additional results in our paper.
***
2. **Practical and effective BAU framework**: We propose a simple yet effective approach that mitigates polarized effects without requiring complex training procedures. BAU achieves state-of-the-art performance while maintaining simplicity and efficiency. Based on the concepts of alignment and uniformity, we further propose *novel components specifically tailored for this task*: a novel weighting strategy and a domain-specific uniformity loss.
***
> **Note**: In Table B, we have validated the effectiveness and broad applicability of BAU on several different baselines: unsupervised approach, other backbones, and loss functions. We will include these additional results in our paper.
***
3. **Extensive experimental results and analysis**: We have provided comprehensive experimental results including quantitative and qualitative analysis:
    * Tables 3-4 demonstrate the superior performance of BAU against other DG re-ID methods.
    * Tables 5-6 and Table A provide extensive ablation studies validating the effectiveness of each component in BAU.
    * Tables 7-8 and Table C show the robustness of our method against different augmentation configurations.
    * Figures 4-5 provide quantitative and qualitative analyses validating the effectiveness of our novel components: the weighting strategy and domain-specific uniformity loss.
***
> **Note**: Figure C further quantitatively analyzes our novel weighting strategy across varying augmentation probabilities, demonstrating its crucial role in enabling the effective use of strong augmentations. We will include these additional analysis in our paper.
***

We believe comprehensive analyses and additional insights (e.g., commonality of the polarized effects and broad applicability of the proposed method) significantly strengthen our work's impact in the field of DG re-ID.

Once again, we sincerely appreciate your time and valuable feedback in improving our work.

Best regards,

Authors of submission 16440
Dear Reviewer Ywj9, 

We are deeply grateful for your time and efforts during the discussion period. 

Throughout this period, our further explanations with additional results have addressed the concerns raised by reviewers, leading to positive feedback which has been very encouraging for us.

---
We would like to further clarify our improvements made during the rebuttal period:

* Further investigation of polarized effects: We have conducted additional experiments exploring the commonality of polarized effects across different backbones, loss functions, and augmentation types (Figures A and B). These results provide further support for our findings and intuitions regarding polarized effects.

* Broad applicability of BAU: We have investigated the effectiveness of BAU on various baselines, including unsupervised approaches, different backbones, and loss functions (Table B). These experiments validate the versatility and potential applicability of our method.

* More analysis of the weighting strategy: We have provided an in-depth analysis of our novel weighting strategy, examining its behavior across varying augmentation probabilities (Figure C). This analysis presents additional insights into the robustness of our approach.

* Clarification of our work: We have provided a more detailed explanation of how our method uniquely applies concepts of alignment and uniformity to the specific challenges of DG re-ID, distinguishing our approach from other existing studies.

* Additional exploration of our design choices: We have conducted further analysis and comparisons in Tables A and C, which aim to demonstrate the effectiveness of BAU across different augmentation configurations and its performance relative to existing methods.

We will incorporate these results into our revision.

---

We are particularly thankful for your insightful comments, which have greatly contributed to enhancing our work. Your feedback inspired the valuable analysis of the alignment loss presented in Figure C and several important comparisons with existing works, which greatly strengthen our paper.

As the discussion period is drawing to a close, if there are any additional points that you think could be clarified in our work, we would be happy to provide additional information promptly. 

Once again, we sincerely appreciate your valuable feedback and insights in improving our work.

Best regards, 

Authors of submission 16440
Dear Reviewer fzqs,

We are deeply grateful for your time and valuable feedback throughout this review process. Your insightful comments have significantly contributed to improving our work. They have led to important clarifications and comprehensive analysis & comparisons, which greatly strengthen our work.

As the discussion period is drawing to a close, we would like to kindly follow up on our previous response. We sincerely invite you to share any remaining concerns, particularly regarding the novelty of our work and the comparisons with recent methods, for your further consideration. We appreciate your feedback and would like to discuss these points further.

---

In addition to our previous response, we would like to further clarify key contributions of our work, which we believe represent significant innovations in the field of DG ReID:

1. Insightful investigation of polarized effects in DG re-ID, a phenomenon previously overlooked.
2. A simple yet effective BAU framework that mitigates these effects without complex procedures.
3. Novel components tailored for this task: a weighting strategy for alignment loss and a domain-specific uniformity loss.

During the discussion period, we further confirmed and strengthened our contributions through additional experiments:

- We verified the commonality of the polarized effect across different backbones, loss functions, and augmentation types (Figures A and B).
- We demonstrated the broad applicability of BAU on various baselines, including unsupervised approaches, different backbones, and loss functions (Table B).
- We provided an in-depth analysis of our novel weighting strategy, showing its effectiveness across varying augmentation probabilities (Figure C).

These additional results further validate our initial insights and the effectiveness of our proposed method, and we will incorporate this in our revision.

---

Regarding the comparison with recent methods, we have conducted additional comparisons with more recent methods, ISR (ICCV 2023) and StyCon (MM 2023). We are open to further comparisons if there are other specific methods we should consider, and will incorporate this feedback in our revision.

---

If there are any aspects of our work that you think could benefit from further elaboration or clarification, we would be happy to provide additional information promptly.

Thank you again for your valuable contribution to our research. We look forward to any additional feedback you might have that would be valuable to our work.

Best regards,

Authors of submission 16440
Dear Reviewer 4LRb,

Thank you for your thoughtful consideration and for raising the score.
We are pleased that our responses have addressed your concerns.

We will include the additional experimental results based on your valuable feedback (e.g., the polarized effects across different backbones, losses, and augmentations) in our revision.

If you have any remaining questions or feedback, we would be glad to provide additional clarifications or results.
Please don't hesitate to let us know.

We sincerely appreciate your valuable feedback and your time in improving our work.

Best regards,

Authors of submission 16440
Dear Reviewer L6wV,

Thank you for your thoughtful consideration and for raising the score.
We are glad that our responses have addressed some of your concerns. 

If you have any remaining questions or feedback, we would be glad to provide additional clarifications or results if needed. 
Please don't hesitate to let us know.

We sincerely appreciate your valuable feedback to improve our work.

Best regards,

Authors of submission 16440
Thanks for your responses. The author has answered my major concerns, and I will change the score to weak accept.
Thanks for your responses. The author has answered some of my doubts, and I will change the score to borderline accept.
Dear Reviewer Ywj9,

Thank you for your feedback and for taking the time to consider our responses.
We're pleased that our responses have helped to address some of your concerns.

If you have any remaining specific concerns or doubts, please let us know.
We would be glad to provide additional clarifications or results for these points if needed.

Once again, we appreciate your valuable time and comments, and we look forward to the opportunity to further improve our paper based on your insights.

Best regards,

Authors of Submission 16440
Thanks for your responses. The author has answered some of my doubts, and I will maintain my score, combined with the comments of other reviewers.
Dear reviewer 4LRb,

As the reviewer-author discussion period is coming to a close, we kindly ask if there are any remaining concerns or points about our submission that we haven't sufficiently addressed. 
We're ready to provide additional clarifications or information if needed.

Once again, we appreciate your valuable efforts and feedback to strengthen our work.

Best regards,

Authors of submission 16440
Dear reviewer Ywj9,

As the reviewer-author discussion period is coming to a close, we kindly ask if there are any remaining concerns or points about our submission that we haven't sufficiently addressed. 
We're ready to provide additional clarifications or information if needed.

Once again, we appreciate your valuable efforts and feedback to strengthen our work.

Best regards,

Authors of Submission 16440
Dear reviewer L6wV,

As the reviewer-author discussion period is coming to a close, we kindly ask if there are any remaining concerns or points about our submission that we haven't sufficiently addressed. 
We're ready to provide additional clarifications or information if needed.

Once again, we appreciate your valuable efforts and feedback to strengthen our work.

Best regards,

Authors of submission 16440
Dear Ethics Reviewer MBXr,

We appreciate the thorough ethics review of our work. 
We are glad that the reviewer found our acknowledgment of potential ethical considerations in the Broader Impacts section to be sufficient. 
We recognize the importance of addressing privacy concerns in person re-identification research and will continue to emphasize responsible use and development of these techniques.

Best regards,

Authors of submission 16440
Dear Reviewer fzqs,

We sincerely appreciate your reconsideration and raising the score. We will clarify the training instability associated with meta-learning and domain-adversarial learning and provide proper references in our revision. We would like to address the remaining concerns and highlight the significance of our work.

---

### **Comparison with recent methods**
We appreciate your valuable feedback about comparison with the most recent methods. In our revision, we will include comparisons with state-of-the-art approaches published in 2023, such as ISR (ICCV 2023) [14] and StyCon (MM 2023) [a]. Please note that the results of ISR are from ResNet-50 trained on large-scale unannotated videos (47.8M person images from 74K video clips). Meanwhile, StyCon used the same training dataset as BAU (Market-1501, CUHK02, CUHK03 and CUHK-SYSU).  A comparison with more recent methods (mAP/Rank-1) is shown below:

| Method            | PRID           | GRID           | VIPeR          | iLIDs          | Average        |
|-------------------|----------------|----------------|----------------|----------------|----------------|
| ISR (ICCV 2023)    | 70.8 / 59.7    | 65.2 / 55.8    | 66.6 / 58.0    | **91.7 / 87.6**| 73.6 / 65.3    |
| StyCon (MM 2023)   | **78.1 / 69.7**| 62.1 / 53.4    | 71.2 / 62.8    | 84.8 / 78.0    | 74.1 / 66.0    |
| **BAU (Ours)**     | 77.2 / 68.4    | **68.1 / 59.8**| **74.6 / 66.1**| 88.7 / 83.7    | **77.2 / 69.5**|

We further compare ISR with the proposed BAU on large-scale datasets.
We report BAU results trained under Protocol-2 (P-2) and Protocol-3 (P-3).

| Method              | CUHK03          | MSMT17          | Market-1501      | Average         |
|---------------------|-----------------|-----------------|------------------|-----------------|
| ISR (ICCV 2023)     | 26.1 / 27.4     | 21.2 / 45.7     | 65.1 / 85.1      | 37.5 / 52.7     |
| **BAU (P-2, Ours)** | **42.8 / 43.9** | **24.3 / 50.9** | **77.1 / 90.4**  | **48.1 / 61.7** |
| **BAU (P-3, Ours)** | **50.6 / 51.8** | **26.8 / 54.3** | **79.5 / 91.1**  | **52.3 / 65.7** |

The results show that our method remains superior to these recent approaches.

In summary, our current comparison aims to be comprehensive, including recent state-of-the-art methods.
If you have *any suggestions of other recent methods that should be compared to our work*, we would sincerely appreciate your recommendations.

---

### **Regarding innovation of BAU**

We appreciate the valuable feedback in clarifying the innovations of the proposed method.
We would like to clarify several key innovations of our work:
- First to analyze and address the critical issue of polarized effects by data augmentations in DG ReID, which has been overlooked in previous studies.
- Novel weighting strategy for alignment loss to assess the reliability of augmented samples.
- Domain-specific uniformity loss for enhancing domain-invariant feature learning.
- Enabling effective use of strong augmentations, previously challenging in DG ReID.
- Achieving state-of-the-art performance without complex training procedures.

While building on existing concepts, BAU's unique application and novel components significantly advance the field of DG ReID.
Furthermore, as shown in Table B in 1-page PDF, we demonstrate that the proposed method is applicable to various existing methods.
We believe this contribution will substantially impact future research in the field.

We would be grateful if you could give further consideration to these points.
We appreciate your thorough review and remain committed to improving our manuscript based on your valuable feedback.

Best regards,

Authors of submission 16440

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[a] Style-Controllable Generalized Person Re-identification, MM 2023
Thanks to the authors for answering some of my concerns, I will change the score to Borderline Reject. the main reason for this is that the actual innovations in this paper are limited and the methods compared are not new enough. In addition, as the authors state, it is the meta-learning and adversarial learning based methods that suffer from the problem of training stability, and the authors need to add these constraints to their descriptions.
Here, we provide discussions with additional experimental results according to comments in the **Questions**.

### **Regarding different strategies of alignment loss**

Based on the suggestion, we investigate the impact of various alignment strategies in Table A. Specifically, we compare the performance of minimizing the feature distance between augmented and original images of the same instance ($\| \tilde{f}_i - f_i \|$), between original images of positive pairs ($\| f_i - f_j \|$), and between augmented and original images of positive pairs ($\| \tilde{f}_i - f_j \|$, Ours). The results show that our strategy, which learns invariance between original and augmented images of positive pairs, achieves the best performance.
This result demonstrates that BAU effectively learns invariance between the original image and the augmented image.

### **Regarding domain-specific uniformity loss with corresponding class prototype**

This point brought up by the reviewer is indeed correct, and we appreciate the reviewer for bringing this to our attention.

In our actual BAU implementation, $f_i$ and $\tilde{f}_i$ are indeed pushed against only the class prototypes within the same domain that do not belong to its own class, aligning with your intuition.
This approach stably encourages separation between different identities while maintaining cohesion within the same identity class.

Thus, the explaination of line 214 should be:

> where $\mathcal{N}(\mathbf{f})$ is the index set of *nearest prototypes of $\mathbf{f}$ that are from the same source domain and different class prototypes*, ...

This approach is more effective as it encourages separation between different identities while maintaining the cohesion within the same identity class.

We apologize for the lack of clarity in our original description and will ensure this is accurately reflected in our revision.

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We hope our responses have addressed your concerns. We welcome any further questions or discussions about our work.

Best regards, 

Authors of submission 16640