Generalizable Person Re-identification via Balancing Alignment and Uniformity
Authors: Yoonki Cho
Jaeyoon Kim
Woo Jae Kim
Junsik Jung
Sung-eui Yoon
Venue: NeurIPS 2024 poster (2024)
Keywords: re-identification
domain generalization
image retrieval
Primary area: machine_vision

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. The code is available at https://github.com/yoonkicho/BAU.

Generalizable Person Re-identification via Balancing Alignment and Uniformity
Yoonki Cho
Jaeyoon Kim
Woo Jae Kim KAIST
Junsik Jung
Sung-Eui Yoon
Abstract
Domain generalizable person re-identification (DG re-ID) aims to learn discrimina- tive representations that are robust to distributional shifts. While data augmentation is straightforward solution improve generalization, certain augmentations hibit polarized effect in this task, enhancing in-distribution performance while deteriorating nul-of-distribution performance. In this paper, investigate this phenomenon and reveal that it leads to sparse representation spaces with reduced uniformity. To address this issue, we propose novel framework, Balancing Align- ment and Uniformity (BAU), which effectively mitigates this effect by maintaining balance between alignment and uniformity. Specifically, BAU incorporates align- ment and uniformity losses applied to both original and augmented images and integrates weighting strategy 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 en- hancing the learning of domain-invarian 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 perfor- mance without requiring complex training procedures. The code is available at https: // thub. com com/yoonkiche cho/AU..
1 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 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-Il) methods often leverage advanced network architectures, such feature nor- malization modules [34-6,, 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 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 in the
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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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-1I). 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 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 aligoment 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-specifee within each source domain, enhancing the learning of uniformity loss to promote uniformity features. Consequently, BAU effectively exploits the advantages of data augmentation, which previous studies could not fully utilize, and achieves state-of-the-arr 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 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.
2 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, 61, 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 statisties 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 invariant representations, several studies [10, 61, 71, 90] leverage domainn-dverssoe training [20, 41] or meta- meta-lcarning [18, 40]. DDAN [6] utilizes domain-wise adversarial feature learning to reduce domain discrepancies for domain invariance. M [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- nomain-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-lcarning [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 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-IID 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-invarian representations.

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KSACHOO (M0000)
2.8
00.
Uniformity (-Cuerum)
Aligmm (CCage)
mulp (%6
2.76
map PM 6
244
902
522
B-1A
Am35
3.
Soure (
Turce (ites
Augmentation probability
Uniformity (Custam)
Augmentation probabillty
(a)
(b)
(c)
Figure I: Analysis on polarized effect of data augmentations on in-distribution (ID) and out-of- distribution (OOD). (a) mAP (%) on Market- of models trained on the same dataset (ID) and MS+CS+C3 (OOD) with varying augmentation probabilities.
(b) Alignment (Caliga) and uniformity (Cuniform) of OOD scenarios (MS+CS+C3 + M). Counterintuitively, augmentations lead to more alignment but less uniformity, indicating that the model fails to sufficiently preserve the diverse (c) Uniformity (- Lumiform)) vs. augmentation probability for the source and target datasets in MS+CS+C3 ~ M. Higher probabilities result in less uniformity, especially under distribution shifts, indicating insufficiency in representing OOD data.
information from the data distribution.
3 Method
Problem Formulation. Given set of K source domains, Ds (Dx) each domain Dj ((o,,y)) }=) consists of images 2; and corresponding identity labels yt. where N₂ denotes the number of images in source domain Dr. Using these source domains, parameterized by 0, to extract person representations f; fo(x.) E RRd from the image Ts, where d is the dimensionality of the representation space. The trained model is then evaluated domain Dr 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, is open-set retrieval task where trained models need to identify unseen classes.
train model fo.
target
3.1 Polarized Effect of Data Augmentation In- and
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-r--distiibution 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-of- distribution (OOD) scenarios, conduct experiments using varying augmentation probabilities (i.e., the probability of applying data augmentation to input images). For data augmentations, we employ the widely used RandAugment [1 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- cooss-y 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-SYST (CS) [85] are used for model training as source domains, and Market-150h (M) [95] as the target domain.
Fig. 1a compares the performance on the Market-1500 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.
Mooe details provided in the appendix.
3

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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 representa- tion space, we leverage the concepts of alignment and uniformity [81], which are key properties of feature distributions the unit hypersphere. Alignment is defined as the expected distance between positive pairs:
Lalign
- log
E (1.))pPpp
Ilf.
(1)
pM
where Ppes is the distribution of positive pairs. Uniformity, on the other hand, is defined by the logarithm of average pairwise Gaussian potential:
Image p-0.0
p-05
p-
Figure 2: Grad-CAM [68] across different probabili- ties of data augmentations.
Cuniform A log IE
[e- 2--2,---l1
(2)
(i.j)~Pam
where Pasta 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. lb illustrates the alignment and uniformity of representations in OOD scenarios 15rket-1501 for MS+CS+C3 + Market-15011 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. Ic, the feature embeddings become increasingly less uniform with higher probabilities, and this becomes more evident distributional shifts occur, as indicated by the red plot in the same figure. 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 performance
while they enhance in-distribution performance, they can deteriorate by leading to a sparse representation space. Nevertheless, data augmentations remain 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.
3.2 Balancing Alignment and Uniformity
We introduce 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 input batch, we generate augmented views of the images, ã = t(x). where I. ~ T denotes augmentations sampled from the distribution T. Our model then extracts features from both the original and augmented images, denoted as f, = (x.)) and f. fo (,:), 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 discrininability by promoting invariance to diverse augmentations, while the uniformity loss encourages generalizability by striving for uniform distribution of features on the hypersphere, thereby preserving diverse visual information from the data.

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Emb:oding Y Protonpc
Feature
Edomain
Encoder fo
Prototype c
Input
Shared
Latien
Cuniform
(b) Lalign & uuttform
Encoder
Feature i
Ldomain
Input
(a) Balancing Aligmment and Uniformity (BAU)
(c) Ldomain
Figure 3: Overview of the proposed framework. In (b) and (c), each color represents a different identity and domain, respectively. (a) With original and augmented images, we apply alignment and uniformity losses to balance feature discriminability and generalization capability. We further introduce a domain-specific uniformity loss to mitigate domain bias. (b) Lalign pulls positive features closer, while uniform pushes all features apart maintain diversity. (c) Ldomain uniformly distributes
each domain's 's features and prototypes, reducing domain bias and thus enhancing generalization.
Alignment Loss. We reformulate the alignment loss minimize the expected feature distance of
positive pairs between original and augmented images, defined as:
Lalign
Σ -
Ipps
(T
where Ipos = [(iij) yi } 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 trans- formations introduced by data augmentations, thereby enhancing feature discriminability. However, some augmented samples may suffer from significant corruption due aggressive augmentations, and learning invariance with these samples can potentially degrade the training process.
To address this issue, we introduce 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], compute the weight as the Jaccard similarity of k-reciprocal nearest neighbors between the augmented sample and the original sample, defined by:
n R4
Wig
[0, 1],
where (f,) the set of indices for k-reciprocal nearest neighbors within a mini-batch of the feature f, Intuitively, a low weight implies that the augmented feature f, and the original feature f, 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:
Lalign
Σ (1
Tej f. f
where w Ep. way
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:
Luniform = loog
2|
11
+ log
Σ c -
(6)
[Taatal
[Taatal
(i.))
(1.))ZZan
where Iaat {(i,]) * j} the index set of all distinct pairs within mini-batch. By incorporating the uniformity loss, the learned representations can be uniformly distributed on the
5

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hypersphere, maintaining the diversity of the feature space. This feature diversity is crucial for the generalization capability, as prevents the model from overfitting to sparse dominant representations and encourages learning subtle cues that can be generalized across different domains.
Dppaif- 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 = {CI, CN E IR-xd with class prototypes с E IRd, where 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:
C cr --- (1- p.
where f is the extracted features of original images with the same class label as c, and μ [0, is the momentum coefficient. To mitigate the inherent domain bias in simple uniformity, we additionally uniformity loss, which enhances the uniformity between the features and
apply a domain-spectie prototypes within their corresponding domain as follows:
=log Ei (Ei e -11i ) + log /EiSENTRE e 2116, - E,
Ldomain
Σ, N
where ((f) is the index set of nearest prototypes of 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 mini- batch. This loss attempts 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:
LBAU Lce + Liri + Aaalign + Luniform + Ldomains
where Len and Liri are the cross-entropy loss and triplet loss, respectively, applied only to the original images. Here, À 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 simultancously exploiting the diversity they introduce. Morcover, BAU is a simple yet effective framework that directly regularizes the representation space without the need for advanced network architectures or complex training procedures.
4 Experiments
4.1 Datasets and Evaluation Protocols
Table 1: Statistics of the used datasets.
Table 2: Evaluation protocols.
Daraset
#TD
#image
aCamem
Setting
Training Data
Testing Data
MSMT17 (MS) [83]
Market1501 (M) [95]
4,101
1,501
126,441
32.217
15
Protocol-1
Full-M-C2+CHHS)
PRID, GRID,
VIPeR, ILIDs
CUHK02 (C2) [43]
1,816
7,264
CUHK03 (C3) [44]
1,467
14,096
2
McMS+CS
C3
Protocal-2
M+CS+C3
MS
CUHK-SYSU (CS) [85]
11,934
MS+CS-C3
PRID [31]
200
1.134
2
GRID [55]
Full-M+MS--CS)
VIPeR [26]
632
1,264
2
Protocal.3
(M-CS()
MS
ILIDs 197]
119
Full-(MS-CS+C3)
We conduct experiments using the following datasets: Market- [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- 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-
Following previous studies [71, 86, 90, 94], we evaluate the proposed method across three protocols, as detailed in Table 2. For Protocol-1, 11, 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-IID datascts, specifically PRID, GRID, VIPeR, and iLIDs. The final performance on these small-scale

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Table 3: Comparison with state-of-the methods on Protocol-1. Since DukeMTMC-reID [98].
denoted as D in the table, has been withdrawn, it is not utilized for our training.
PRID
GRID
VIPeR
iLIss
Average
Method
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
DIMN [71]
52.0
39.2
41.1
29.3
60.1
51.2
78.4
70.2
57.9
47.5
DualNorm [34]
64.9
60.4
45.7
41.4
58.0
53.9
78.5
74,8
61.3
57.6
SNR [36]
66.5
52.1
47.7
40.2
61.3
52.9
89.9
84.1
66.4
57.3
DDAN [6]
67.5
62.9
50.9
78.0
65.1
+C2+C3
RaMoE [12]
67.3
$7.7
54.2
46,8
56.6
90.2
85.0
62.0
61.5
+CS
DMG-Net [4]
68.4
60.6
56.6
51.0
60.4
53,9
83.9
79.3
67.3
61.2
GDNerm [53]
79.9
72.6
63.8
55.4
74.1
66.1
87.2
81.3
76.3
68.9
DIIN [35]
79.7
71.0
60.6
51.8
70.7
62.9
87.2
81.8
74.6
66.9
StyCon [48]
78.9
71.0.
60.4
50.7
74.4
66.8
86.9
80.7
75.2
67.3
QAConigo [50]
62.2
52.3
57.4
48.6
06.3
$7.0
81.9
75.0
67.0
58.2
MºL [94]
65.3
55.0
50.5
40.0
68.2
60.8
74.3
65.0
64.6
55.2
MetaBBN [10]
M
META
71.7
61.9
60.1
70.9
+C2+C3
ACT. [90]
73.4
63.0
65.7
55.2
75.1
66.4
86.5
81.8
75.2
66.6
+CS
StyCon [48]
78.1
69.7
62.1
53.4
71.2
62.8
84.8
78.0
74.1
66.0
BAL (Ours)
77.2
68.4
68.1
59.8
74.6
66.1
88.7
83.7
77.2
69.5
Table 4: Comparison with state-of the-rrr methods on Protocol-2 and Protocol-3.
McMS+CS C3
M+CS+C3
MS
MS+CS+C3 M
Average
Setting
Method
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Runk-1
[36]
6.8
34.6
30.5
QAConizo [50]
16.4
Mº [94]
16.7
61.5
Protocol-2
MetaBIN [10]
28.8
28.1
17.8
57.9
34.8
49.5
META [86]
36,3
35.1
22.5
49.9
67.5
57.0
ACL 190)
41.2
41.8
20.4
45.9
74.3
89.3
59.0
BAU (Ours)
42.8
43.9
24.3
50.9
77.1
90.4
48.1
61.7
SNR [36]
17.5
17.1
7.7
22.0
52.4
77.8
25.9
39.0
QAConvoo [50]
32,9
33.3
17.6
46.6
66.5
85.0
39.0
55.0
M [94]
35.7
36.5
17.4
38.6
62.4
82.7
38.5
52.6
MetaBIN [10]
43,0
43.1
18.8
41.2
67.2
84.5
43.0
56.3
Protocel-3
META [86]
47.1
46.2
24.4
52.1
76.5
90.5
49.3
62.9
ACL [90]
49.4
50.1
21.7
47.3
76.8
90.6
49.3
62.7
BAU (Ours)
50.6
51.8
26.8
54.3
79.5
91.1
52.3
65.7
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 leave-one-od evaluation setting with four large-scale scale datasets: M, MS, C3, and CS. Three datasets are used the source domain, and the remaining one 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 person search dataset with single camera view, it is only used for training.
4.2 Implementation Details
Following previous studies [34, 50, 51, 86, 90], use ResNet-50 [29] pre-trained 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 512, including both original and augmented images. Random flipping, cropping, erasing [101], RandAugment [1] []. and color jitter are used for data augmentation. We train the model for 60 epochs using Adam [38] with a weight decay of 5 x 10 The initial learning rate is warmup strategy is applied during the first 10 epochs. The momentum 4 is set to 0. We empirically the weighting parameter
3.5 10-4 and is decreased by factor of 10 the 30th and 50th epochs. A
1.5 and & for the weighting strategy 10. We implement our framework in Py Torch [64] and utilize two RTX-3090 GPUs for training.
4.3 Comparison with State-of-the-Arts
We compare our method with fate-ooftth-art DG re-ID methods Protocol-1, with the results presented in Table 3. We also report the results previous studies that use DukeMTMC-reID [98] for training, denoted as D in the table, while exclude this dataset from training since has been
7

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Table 5: Ablation study of loss functions for augmented images.
M+MS+CS - C3
M+CS+C3 + MS
MS+CS+C3 - M
Average
Method
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1 1
Baseline (wlo augmensed images)
39.2
38,9
18.9
44.8
68.3
86.3
42.1
56.7
Lee
32.1
31.9
18.0
41.7
63.1
83.8
37.7
52.5
Lattin
41.8
42.2
23.1
47.4
73.8
87.4
46,2
Calign + Craniform
46.9
47.4
25.3
51.1
78.1
89.5
50.1
62.7
Cation + Lranffor + Ldomato
50.6
51.8
26.8
54.3
79.5
91.1
52.3
65.7
Table Ablation study of the weighting strategy and the domain uniformity loss.
Weighting
Domuin-specifie Prototype of Ldomain
M+MS+CS + C3
McCS+C3 -> MS
MS+CS+C3 - M
Averuge
Strategy of Latign
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
46.2
46.6
23.9
49.3
76.7
90.0
48,9
62.0
47.1
47.4
24.7
50.9
78.3
88.9
50.0
62.4
49.5
51.0
25.1
$1.6
78.6
90.8
51.1
64.5
50.6
51.8
26.8
54.3
79,5
91.1
52.3
65.7
taken down. As shown in the table, although our method utilizes fewer datasets for training, it surpasses prior state-of-the-art methods in average mAP/Rank-I performance across the four datasets. Comparisons of the proposed method with state-of-the-ar methods on Protocol-2 and Protocol-3 are presented in Table The results demonstrate that our method outperforms other methods, confirming the generalization capability of BAU on scaee datasets. Specifically, we achieve higher average mAP scores across three datasets than the previous ofate-of-the-ar 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 archi- tectures [12, 86, 90]. Specifically, BAU is 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.
4.4 Ablation Study and Analysis
To evaluate the effectiveness of each component 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-entropp and triplet losses without any augmented images (when p 0 of Sec. 3.1), serves as a reference point. Applying only the cross cooss-entropy loss Lce to the augmented images leads to a performance drop, aligning with our analysis in See. 3.1 that naive training with augmented images can degrade generalization performance. This result also aligns with studies demonstrating that cross- entropy loss easily overfit biases and noise in the data [25, 59, 92]. When applying the proposed alignment loss Lalign. the performance improves, surpassing the baseline. Further incorporating the uniformity loss Luniform significantly boosts the performance, confirming the importance of balancing both alignment and uniformity to achieve better generalization. Finally, integrating the domain-specific uniformity loss Ldomain achieves the best performance, with notable improvements of + +T1.0%//48 %.00% 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 validate the effectiveness of the proposed weighting strategy for the alignment loss and the domain--pecifee 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 -smeiie uniformity loss, we apply simple uniformity loss with a feature memory bank without considering each domain (i.e., modifying Eq. (8) with nearest prototypes in
8

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CINEDO 13 1030
CU--00 C2.370M
2-18
1w00
Alignment (2
cm
2-120
125
470
Uniformity (Cuntore)
without Edomain
with Edomain
(a)
(b)
Figure 4: Analysis of alignment and uniformity. (a) Alignment (Calign) ) and uniformity (Cuniform) on Market-1501 when MS+CS+C3 - M under Protocol-3 with varying augmentation probabilities. (b) T-SNE visualization with and without the domain-speciffi uniformity loss Laomain- The values in parentheses in each legend label indicate the uniformity of the corresponding domain.
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 achieve strong performance in the DG re-ID task by focusing 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 bascline 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 O), 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-specifie uniformity loss. To explore the effect of the domain-speen -spccifc 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 Ldomain- As shown in Fig. 4b, without Ldomain- the learned features exhibit domain-specific clusters, indicating lack of domain invariance. On the other hand, applying Ldommin results in 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 representations.
each domain to learn
To further analyze the proposed weighting strategy for the alignment loss, we conduct quantitative comparisons across varying augmentation probabilitics, with and without the weighting strategy. As shown in Fig. 5a, 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, 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 reliable pairs between original and augmented images.
For qualitative analysis, Fig. 5b illustrates the weight scores w Eq. (4) for the alignment loss. The top row shows the original images, while the bottom row represents their augmented versions

---

80
79.1
79.5
80.1
Augmented Original Image
8.0
mAP (%)
75
70
71.4
70.3
W/ weighting strategy
w/o weighting strategy
0.5
Augmentation probability
w 0.0
30 0.13
1 0.26 w 0.87
(a)
(h)
Figure 5: Analysis of the weighting strategy. (a) Quantitative comparison of mAP (%) across varying augmentation probabilities, with and without the weighting strategy, on MS+CS+C3 - M under Protocol-3. The weighting strategy consistently improves performance, especially at higher augmentation probabilities, where the mAP drops significantly without it. (b) Qualitative analysis of the weight score for different pairs of original and augmented images.
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, still has limitations overcome. As a straight- forward 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 balaneing 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, proposed simple yet effective framework, Balancing Aligmment 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-at performance without requiring advanced network architectures complex training procedures. Furthermore, extensive ablation studies and analyses validated the effectiveness each component BAU, emphasizing the importance of balancing alignment and uniformity to achieve robust generalization in person re-identification.
Acknowledgements
We would like to thank the anonymous reviewers for their constructive comments and suggestions. This work was supported by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant (No. RS-2003-00227965, Recognition, Action and Interaction Algorithms for Open-world Robot Service) and the National Research Foundation of Korea (NRF) grant (No. RS- 2023-00208506 (2024)), both funded by the Korea government (MSIT). Prof. Sung-Fui Yoon is corresponding author (e- (e-mail: Langeuikaisse edu).
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A Additional Implementation Details
This section provides additional implementation details for the experiments in Sec. 3.1. RandAugment [1I] includes comprehensive geometrie manipulations and color variations 'AutoContras',, 'Equalize'. 'Invert', 'Rotate', 'Posterize', 'Solarize', 'Color'. 'Contrast', 'Brightness', 'Sharpness', 'ShearX', 'ShearY', 'Translate', "TranslateY', 'Cutout', 'SolarizeAdd'. Since person re-identification fine ame-grained task that requires dis- tinguishing individuals with subtle distinctions, severe color distortion deteriorate feature dis- criminability. 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 x 10 10- The initial learning rate is set 3.5 10 and is decreased by a factor of 10 the 40th and 70th epochs. A warmup strategy is 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.
also applied during the first 10 epochs. For the out-of-distribun
B Additional Experimental Results
A11
DNP 170
(NAP 170
113
Vir'
MoblcketVz
CE+Triglet
POL
Random
(a) Backbone
(b) Loss Function
(c) Augmentation
Figure 6: Analysis on polarized effect across different types of (a) backbones, (b) loss functions,
and (c) augmentations. The experimental configurations are the same in Fig. of the main paper.
1162
Courre Smmar Seteertee
Probabilby
Protasliy P
Probaol
Probel fyy
(a) Random Erasing
(b) RandAugment
Figure 7: Analysis polarized effect of (a) Random Erasing and (b) Rand Augmentt across augmentation probabilities. The experimental configurations are the same in Fig. 1 of the main paper.
B.1
Commonality Analysis of Polarized Effect
To further explore the commonality of the polarized effect in data augmentation for DG re-ID,, investigate its presence across different backbones, loss functions, and augmentation types.
Backbone and loss function. We examine the polarized effect across different backbone architec- tures, including transformer based (ViT-B/16 [16]) and lightweight (MobileNetV2 [67]) networks. Following the same experimental protocol outlined Sec. 3.1, we train these models using cross- entropy and batch-hard triplet loss[30], with and without data augmentations (i.e., Random Eras- ing [101] and RandAugment [1]]). As shown in Fig 6a, the polarized effect is consistently observed across different backbones. Additionally, investigate this effect across different loss functions ResNet-50 backbone, including ArcFace [14] and PCL [23, 42], which are widely used for re-ID and
16

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Table Evaluation of BAU with other backbones and loss functions on Protocol-2.
M+MS+CS C3
M+CS+C3
MS
MS+CS+C3 M
Average
Method
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
BAU with other backbones
MobileNetV2
21.4
20.2
10.1
24.3
46.9
69.4
26.1
38.0
BAU (Ours)
27.4
12.9
31.3
59.1
78.5
33.2
45.7
ViT-B/16
31.8
30.9
14.8
29.2
52.9
74.6
33.2
44.9
BAU (Ours)
37.3
36.9
20.0
40.5
63.5
80.6
40.3
52.7
BAU with other
loss functions
ArcFace
33.8
33.9
17.6
37.6
58.9
79.5
36.8
50.3
BAU (Ours)
23.5
50.8
72.5
88.7
44.6
59.3
PCL
34.6
35.5
16.4
33.7
63.5
83.4
38.2
50.9
BAU (Ours)
39.7
40.9
21.8
47.0
74.3
88.4
45.3
58.8
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 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 Rand Augment can introduce significant distortions (e.g., pixel drops) images, Color Jitter only provides simpler color distortions, which could enhance model robustness to variations in lighting and color conditions in unseen environments. Additionally, shown in Fig. 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).
B.2 Evaluation of BAU across Backbones and Loss Functions
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], lightweight network, and cross-entropy and batch-hard triplet loss [30] with Random Erasing [101] and Rand/ugment [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 effective regardless of underlying architecture.
ViT-B/16 [16], a transformer-basee architecture. For training,
We further evaluate the effectiveness of BAU on models trained with different loss functions, Are- Face [14] and PCL [23, 42]. For training, employ ResNet-50 backbone with Random Erasing and RandAugment for augmentations. As shown in Table. 7, BAU consistently improves the perfor- mance 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 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 simple regularization technique, without requiring
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Table 8: Ablation study of data augmentations on Protocol-3.
M+MS+CS + C3
M+CS+C3
MS
MS+CS+C3
- M
Average
Random Erasing
RandAugment
Color Jitter
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
43,1
42.8
23.1
47.2
72.0
87.1
46.1
59.0
44.3
45.5
23.3
47.4
74.1
88.8
47.2
60.6
51.3
77.1
47.7
25.3
51.7
78.7
90.1
47.9
48.5
25.9
51.9
78.4
90.6
50.7
63.7
49.4
50.2
26.3
53.3
79.0
90.4
51.6
64.6
50.6
518
26.8
$4.3
79.5
91.1
52.3
65.7
Table 9: Ablation study applying probabilities of data augmentations.
M+MS+CS C3
M+CS+C3 + MS
MS+CS+C3 M
Average
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
0.0
43.1
42.8
23.1
47.2
72.0
87.1
46.1
59.0
0.25
48.2
48.6
27.0
55.0
79.1
90.9
51.4
64.8
0.5
50.6
51.8
26.8
54.3
79.5
91.1
52.3
65.7
0.75
49.6
50.1
25.1
51.5
80.1
91.8
51.6
64.5
1.0
47.9
47.6
24.0
49.1
76.
89.1
49.3
61.9
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.
B.3 Ablation Study of Data Augmentation
Table 8 presents an ablation study the impact of different data augmentations the generalization performance of our method under Protocol-3. We investigate three augmentations used in the pro- posed method: Random Erasing [101], RandAugment [1 [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 data augmentations, Specifi- cally, even though Random Erasing has generally been shown to degrade generalization performance previous studies, our method can effectively exploit the advantages this augmentation technique. Furthermore, it is worth noting that even without any augmentations, the proposed method still outperforms the baseline (see Table 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 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 Table increasing the augmentation probability generally leads to better generalization performance up to 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 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 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.
B.4 Parameter Analysis
We conduct 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 & the weighting strategy and the weighting parameter À for the alignment loss on the MS+C3+CS - M setting under Protocol-3.
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51.1
50.9
90.9
30.4
90.7
91.1
90.1
85
87.2
RRank-1
Rannk-1
85
80
79.5
78.9
78.6
80
75.2
75
77.4
77.6
77.3
Rank-1
10
15 20 25 30
1.0
1.5
20
2.5
3.0
3.5
(a)
(b)
Figure 8: Parameter analysis of k and on MS+C3+CS -+ under Protocol-3. (a) mAP/Rank-1 (%) with varying k-reciprocal nearest neighbors for the weighting strategy. (b) mAP/Rank-1 (%) with varying the weighting parameter for the alignment loss.
Figure 8a shows the mAP and Rank-1 accuracy of our method with different values 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 Rank-1 accuracy of 91.1%. However, when & 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 unreliable weights are used. Setting k to 5 too strict for computing reliable weights, 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 method with different values of À for the alignment loss. We observe that setting À to 1.5 yields the best performance. This demonstrates the importance of balancing the contribution of the alignment loss with other loss terms. When is too small, the alignment loss may not sufficiently enforce feature invariance to augmentations. On the other hand, large À may overemphasize alignment and hinder uniformity, leading 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 X 1.5 used in all our experiments reported in the main paper.
C Broader Impacts
The insights behind our method, balancing alignment and uniformity induce feature diversity, could be valuable for improving generalization in other fine- 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-invarian features, the proposed method could mitigate biases (e.g., race, gender) in person re-identification systems by focusing on domain-agnoste 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 tracked without consent as they move through public spaces. Researchers and users of person re-identification technology should be attentive to using it 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.
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Reviews:
[{"review_id": "vOrqBDS687", "reviewer": "Reviewer_L6wV", "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.", "questions": "1.Whether different alignment strategies will affected the effect of the proposed framework?\n2.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.\n3.How about the versatility of the BAU framework? Do these losses apply to the three typical models？", "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.", "rating": 5, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Since data augmentations can induce sparse representation spaces with less uniformity, the authors propose a simple yet effective framework, Balancing Alignment and Uniformity (BAU), which alleviates the polarized effects of data augmentations by maintaining a balance between alignment and uniformity. This work highlights the significant potential of balancing alignment and uniformity to enhance generalization for person re-identification.", "weaknesses": "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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer L6wV,\n\nThank you for your thorough review and constructive feedback. We greatly appreciate your time and effort in evaluating our work. Below, we address your concerns and questions.\n\n### **Regarding the effects of different alignment strategies**\nWe report the impact of various alignment strategies in Table A. Specifically, we compare the performance of minimizing the feature distance between:\n* Original images of positive pairs ($\\| f_i - f_j \\|$)\n* Augmented and original images of the same instance ($\\| \\tilde{f}_i - f_i \\|$)\n* Augmented and original images of positive pairs ($\\| \\tilde{f}_i - f_j \\|$, **Ours**)\n\nThe results show that our strategy, which learns invariance between original and augmented images of positive pairs, achieves the best performance.\nThis result demonstrates that BAU effectively learns invariance between the original image and the augmented image.\n\nFurthermore, as shown in Figure C in the 1-page rebuttal PDF, our weighting strategy for the alignment loss consistently improves the performance across different augmentation probabilities, and the effectiveness of the weighting strategy becomes more pronounced at higher augmentation probabilities.\nThis indicates that focusing on reliable pairs between original and augmented images enhances the learning of robust features. \n\nWe will include this analysis in the revised manuscript to provide a more comprehensive understanding of the alignment loss and the effectiveness of our weighting strategy.\n\n### **Regarding the versatility of BAU and validation on different baselines**\nWe agree that validating the effectiveness of BAU on various types of models would further strengthen our work. \nIn the rebuttal,  we provide additional experimental results applying BAU to five different baselines: \n* Unsupervised approach (Cluster Contrast [a])\n* Lightweight backbone (MobileNetV2 [b])\n* Transformer-based backbone (ViT-B/16 [c])\n* Different loss functions for re-ID \n  * ArcFace [d]\n  * PCL [e, f]\n\nThe results in Table B (in the 1-page rebuttal PDF) show consistent performance improvements across all these diverse baselines, demonstrating the versatility and general applicability of BAU.\nNotably, BAU achieves these improvements as a simple regularization method, without complex training procedures or additional trainable parameters. \nThis highlights its efficiency and potential for easy integration across various baseline models in DG re-ID tasks.\n\nBesides, we would like to clarify that BAU is designed for supervised domain generalization settings, as it requires labels to compute the alignment loss and domain-specific loss. Therefore, directly applying BAU to self-supervised approaches is non-trivial. Nonetheless, to demonstrate the potential of BAU in unsupervised scenarios (Cluster Contrast), we adapt it to work with pseudo-labels obtained from clustering. The results demonstrate improved performance, indicating the potential for extending BAU to self-supervised and unsupervised settings without ground-truth labels in future work.\n\nIn the revised manuscript, we will include the additional experimental results and discussions to provide a more comprehensive evaluation of versatility and applicability to various baselines. We will also clarify the focus on supervised learning settings and the potential for future extensions to self-supervised and unsupervised approaches.\n\nOnce again, we thank you for your valuable feedback and hope that our responses and additional results address your concerns. We look forward to further improving our work based on your suggestions.\n\nBest regards,\n\nAuthors of submission 16640\n\n---\n\n[a] Cluster Contrast for Unsupervised Person Re-Identification, ACCV 2022\n\n[b] MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018\n\n[c] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale, ICLR 2021\n\n[d] ArcFace: Additive Angular Margin Loss for Deep Face Recognition, CVPR 2019\n\n[e] Prototypical Contrastive Learning of Unsupervised Representations, ICLR 2021 \n\n[f] Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID, NeurIPS 2020", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Olm0aNiHkJ", "reviewer": "Reviewer_Ywj9", "summary": "Although data augmentation can improve in-distribution performance, it may lead to a sparse representation space, thereby reducing out-of-distribution performance.\nTo 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. \nBAU achieves state-of-the-art performance on various benchmarks and protocols, and extensive ablation studies validated the effectiveness of each component in BAU.", "questions": "Same to the Weaknesses.", "limitations": "The authors have addressed the limitations and potential negative societal impact of work.", "rating": 5, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The paper demonstrates a well-structured presentation, with a clear outline that effectively communicates the core idea.\n\n2.The paper provides sufficient experimental evidence to support the effectiveness of the proposed method.", "weaknesses": "1.As stated in [1], alignment and uniformity are two key properties related to the contrastive loss. \nCan the polarized effects of data augmentation in DG re-ID be resolved by applying only the contrastive loss? \n\n2.For Alignment Loss, how much does the sample that is corrupted during augmentation affect performance? This seems to be something that can be ignored.\n\n3.The pipeline in this paper is very similar to Weak-Strong Augmentation[2], and the author needs to discuss the difference between them. \n\n[1]Wang, Tongzhou, and Phillip Isola. \"Understanding contrastive representation learning through alignment and uniformity on the hypersphere.\" International conference on machine learning. PMLR, 2020.\n\n[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.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer Ywj9,\n\nThank you for your insightful comments and positive remarks on our paper's structure and experimental evidence. \nWe appreciate your feedback and have addressed your main concerns below.\n\n### **Regarding whether polarized effects can be resolved by contrastive loss**\n\nTo address whether contrastive loss alone could resolve the polarized effects of data augmentation in DG re-ID, we conducted additional experiments comparing BAU with Supervised Contrastive Learning (SupCon) [a].\nThe results (mAP/Rank-1) on protocol-2 are shown in the table below:\n| Method              | CUHK03          | MSMT17          | Market-1501      | Average         |\n|-|-|-|-|-|\n| Baseline            | 33.5 / 33.7     | 16.8 / 35.9     | 63.4 / 83.0      | 37.9 / 50.9     |\n| + SupCon            | 37.9 / 37.6     | 18.2 / 41.5     | 71.8 / 87.0      | 42.6 / 55.4     |\n| + **BAU (Ours)**    | **42.8 / 43.9** | **24.3 / 50.9** | **77.1 / 90.4**  | **48.1 / 61.7** |\n\nWhile SupCon improves performance over the baseline, our BAU consistently outperforms SupCon across all settings. \nThese results suggest that while contrastive loss can help mitigate some polarized effects, our approach of explicitly balancing alignment and uniformity is more effective in addressing this issue in the context of DG re-ID.\n\nIt is worth noting that contrastive learning, including SupCon, has been shown to optimize for alignment and uniformity jointly and asymptotically [b]. In contrast, BAU directly and independently optimizes these properties, allowing for more fine-grained control and effective balancing.\n\n### **Impact of corrupted samples during augmentation in alignment loss**\n\nTo mitigate the impact of corrupted samples, we introduced a weighting strategy for the alignment loss (Eq. 4 and 5 in our paper).\nThis strategy assigns lower weights on the alignment loss computed between original and potentially unreliable augmented samples, and higher weights on that computed between reliable pairs of original and augmented images, thus reducing the influence of potentially corrupted samples.\n\nTo demonstrate the effectiveness of the alignment loss with the weighting strategy, we conducted additional analysis comparing the performance with varying augmentation probabilities, both with and without the weighting strategy. \nThe results are shown in Figure C of the 1-page rebuttal PDF.\n\nThe results show that our weighting strategy consistently improves performance across different augmentation probabilities, with the gap becoming more pronounced at higher probabilities where corruption is more likely. \nFor instance, 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\\%.\nThanks to the proposed weighting strategy, BAU allows learning invariance with reliable augmented samples from informative augmentations.\n\nFurthermore, Tables 7 and 8 in the supplementary material of our main paper demonstrate that BAU consistently improves performance compared to the baseline for various augmentation configurations and probabilities, further validating the effectiveness of our approach in handling potentially corrupted samples.\n\n### **Comparison with Weak-Strong Augmentation**\n\nWe appreciate the constructive comments regarding the relation to existing techniques. \nOur approach is specifically designed to address the unique challenges of DG re-ID, with a particular focus on mitigating the polarized effects of data augmentation.\nWhile our BAU and the Cross-Domain Adaptive Teacher (CDAT) method [c] share a similar spirit in exploiting different levels of data augmentations to improve model generalizability, there are significant key differences:\n\n***Task goal:***\n- CDAT (CVPR 22): Cross-domain adaptation for object detection.\n- BAU (Ours): Domain generalization for person re-ID.\n\n***Framework:***\n- CDAT (CVPR 22): Utilizes a teacher-student framework where the teacher model with weak augmentations guides the student model with strong augmentations in a mutual learning manner.\n- BAU (Ours): Employs a single model framework where the model is trained with both original and augmented images while balancing alignment and uniformity.\n\n***Techniques:***\n- CDAT (CVPR 22): Focuses on generating reliable pseudo labels by the teacher model and utilizes domain-adversarial loss with a domain discriminator.\n- BAU (Ours): Focuses on mitigating polarized effects of data augmentation in DG re-ID and utilizes domain-specific uniformity loss without any domain classifiers.\n\nIn summary, BAU is specifically tailored for domain generalization in person re-ID based on our experimental observations and analysis of alignment and uniformity.\nBased on these insights, we further propose a novel weighting strategy for alignment loss and domain-specific uniformity loss to enhance model generalizability, clearly distinguishing our approach from CDAT's focus on cross-domain object detection adaptation.\n\nWe hope these explanations and additional results clarify the distinctions and advantages of our approach. \nThank you again for your valuable feedback, which has helped us provide a more comprehensive evaluation of our method in relation to existing techniques.\n\nBest regards,\n\nAuthors of submission 16640\n\n---\n\n[a] Supervised Contrastive Learning, NeurIPS 2020\n\n[b] Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere, ICML 2020\n\n[c] Cross-Domain Adaptive Teacher for Object Detection, CVPR 2022", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oKtwRogNfw", "reviewer": "Reviewer_fzqs", "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.", "questions": "See the weakness.", "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.", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "This paper shows sufficient ablation experiments and adequate visualization results are displayed.\n\nThe study on the effect of data augmentation on generalization performance is of interest.", "weaknesses": "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.\n\nThe 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.\n\nIs there any relevant experimental or theoretical support for this paper's claim that current generalization methods affect training stability?\n\nThis 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.\n\nThis paper lacks experimental comparisons with more recent methods and only compares methods from 2022 and earlier.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer fzqs,\n\nThank you for constructive reviews and your time and effort in evaluating our work.\nBelow, we address your concerns and questions.\n\n### **Clarification of alignment and uniformity**\nWe apologize for not sufficiently explaining these concepts in the context of ReID.\n\nFor ReID, alignment aims to learn similar feature representations for positive pairs (same identity) across different conditions such as various poses, viewpoints and augmentations.\nConversely, uniformity aims to distribute feature representations uniformly across the embedding space, spreading out features of different identities and encouraging the learning of diverse visual information.\n\nWhile our method builds upon these concepts in [72], we extend the original idea from self-supervised learning to the DG ReID task.\nFor the first time, we analyze polarized effects caused by augmentations specifically in DG ReID and adapt the concepts of alignment and uniformity to mitigate such effects.\nFurthermore, we introduce novel components specifically tailored for this task: a novel weighting strategy and a domain-specific uniformity loss.\nExtensive ablation studies in our paper (Tables 5-6, Figures 4-5) thoroughly demonstrate their effectiveness.\n\nWe appreciate your feedback and will include this clarification in our revision.\n\n### **Clarification of Figure 1**\n\nWe thank the reviewer for emphasizing this crucial aspect.\n\nFigure 1a shows the polarized effect of augmentation on performance in both in-distribution (ID: M→M) and out-of-distribution (OOD: MS+C3+CS→M) scenarios with varying augmentation probabilities.\nFigure 1c highlights the uniformity of feature spaces for three domains in the OOD scenario (MS+C3+CS→M): \"source (MS+C3+CS) train\", \"source (MS+C3+CS) test\", and \"target (M) test\". Higher augmentation probabilities lead to less uniform feature spaces with sparse representations.\n\nIt is important to note that for ID, the uniformity value (Fig. 1c) is not strictly proportional to the performance (Fig. 1a).\nRather, our key motivation is that uniformity is crucial for performance in OOD.\nAs illustrated in Figure 2 of the main paper, learning invariance with increased augmentations can result in sparse (less uniform) representation spaces that rely on dominant visual information unique to the ID data.\nWhile these sparse representations might generalize well to ID scenarios and thus improve performance, learning diverse, non-dominant visual information (i.e., achieving more uniformity) is vital for OOD performance in DG ReID tasks, where models must handle unseen classes from unseen domains.\n\nThis motivation is further supported by our analysis of OOD performance in relation to alignment and uniformity (Figure 4), which confirms that achieving greater uniformity is essential for OOD generalization.\n\nWe appreciate the reviewer's insight and will enhance the clarity of our presentation and the motivation behind our method in the revised version.\n\n### **Regarding the training instability**\nWe appreciate the valuable comment.\nIn our main paper, we discussed training instability related to domain-adversarial training and meta-learning.\nThe widely-used meta-learning [15] in DG methods is known to face training instability by high-order gradients [a, b].\nSimilarly, several studies have highlighted stability issues in domain-adversarial training  [c, d].\nIn the context of DG ReID, previous works [62, 85] have raised these issues or proposed solutions to these challenges.\nWe will clarify these points and include proper references in our revised version.\n\n### **Polarized effects across augmentation types**\nIn Figure A.(c), we investigate the polarized effect of individual augmentations: Random Erasing (RE), RandAugment (RA), and Color Jitter (CJ).\nThe results show that (1) RE and RA exhibit the polarized effect (improved ID, degraded OOD performance), (2) CJ does not show this polarized effect and generally improves OOD performance, which aligns with previous findings in the field.\nThis is because while RE and RA introduce significant distortions (e.g., pixel drops) to images, CJ provides simple color distortions, enhancing model robustness to variations in lighting and color conditions across unseen environments.\nFig. B confirms that higher probabilities of RE and RA lead to a more pronounced polarized effect, consistent with Fig. 1 in the main paper.\nIn contrast to most existing DG ReID that primarily utilizes augmentations without polarized effects, our BAU successfully exploits the diversity introduced by augmentations regardless of polarized effects.\n\nAdditionally, Fig. A(a) and (b) show that the polarized effect persists across different backbones and loss functions, suggesting it is a general phenomenon in DG ReID.\n### **Comparison with more recent methods**\nThank you for your valuable comments.\n\nWe acknowledge advancements such as [14] and [54] in DG ReID. For instance, [14] uses large-scale unannotated videos, and [54] introduces the Part-Aware Transformer. However, direct comparison is challenging due to differences in experimental protocols. Despite this, we believe proposed BAU is complementary and could be integrated with these advanced methods. Table B shows BAU's effectiveness with unsupervised methods using pseudo-labels and ViT backbones.\n\nWe have identified recent work [e] introducing a style mixing module within a ViT backbone using Protocol-1 and will include a comparison in our revised manuscript.\n\nWe appreciate your feedback and are open to include comparisons with other recent methods that align with our protocols.\n\nBest regards,\n\nAuthors of submission 16640\n\n---\n[a] How to train your MAML, ICLR 2019\n\n[b] On First-Order Meta-Learning Algorithms, arXiv:1803.02999\n\n[c] Free Lunch for Domain Adversarial Training: Environment Label Smoothing, ICLR 2023\n\n[d] A Closer Look at Smoothness in Domain Adversarial Training, ICML 2022\n\n[e] Style-Controllable Generalized Person Re-identification, MM 2023", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "hSxYA7AqJy", "reviewer": "Reviewer_4LRb", "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.", "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 $f_j$?\n- 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": "The limitations on experiments for very large domain shifts and more advanced augmentation methods are discussed.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "- The motivation of the work is clear and well described. It is found that the data augmentations for ReID models can have negative effects on out-of-distribuiton data in despite of their positive effects on in-distribution data. This is a significant problem in domain generalizable ReID. Although this finding is not novel one, the insights provided by the analysis in terms of the alignment and uniformity concepts [1] are appreciated.\n- The proposed framework is simple and effective. The proposed loss functions are designed to address the polarized effect of the data augmentation. These losses are proper application of the alignment loss and uniformity loss [1] to the domain generalizable ReID task with some modifications.\n- The empricial results show that the proposed method is promising.\n\n[1] Wang and Isola, Understanding contrastive representation learning through alignment and uniformity on the hypersphere, ICML, 2020.", "weaknesses": "# Major concerns\nIn 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.\n- 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]).\n- 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.\n\n# Minor concerns\n- 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.\n- 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.\n- 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?\n\n[1] Wang and Isola, Understanding contrastive representation learning through alignment and uniformity on the hypersphere, ICML, 2020.  \n[2] Liao and Shao, Interpretable and Generalizable Person Re-identification with Query-Adaptive Convolution and Temporal Lifting, ECCV, 2020.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer 4LRb,\n\nWe sincerely appreciate your thorough review and are grateful for your positive remarks on the motivation and insights behind our paper. We have addressed your main concerns below.\n\n### **Regarding the polarized effects across different backbones, losses and augmentations**\n**Backbones and loss functions**:\nWe investigated the existence of the polarized effect on different types of backbones, specifically transformer (ViT-B/16) and lightweight (MobileNetV2) networks.\nWe trained these models using CE+Triplet loss, with and without Random Erasing (RE) and RandAugment (RA), and the results are shown in Fig. A (a).\nAdditionally, in Fig.A (b), we explored different types of loss functions, specifically ArcFace[a] and PCL[b], which are widely used for re-ID and retrieval models, on ResNet-50 with and without RE+RA.\nThe results demonstrate that the polarized effect is consistently observed across all tested backbones and loss functions, suggesting that the polarized effect is not limited to the specific architecture and loss function shown in Fig. 1 of the main paper.\n\n**Augmentation types**:\nWe investigated the existence of the polarized effect of individual augmentation method used in BAU: RE, RA, and CJ. \nWe utilized ResNet-50 backbone with CE+Triplet loss, whose results are shown in Fig. A (c).\nWe can see that the performance decrease on the unseen domain is mainly due to the polarized effects observed in RE and RA, while CJ dit not show such behavior, aligning with previous findings in the field.\nWhile RE and RA, which introduce significant distortions (e.g., pixel drops) to images, CJ provides simple color distortions, enhancing model robustness to variations in lighting and color conditions across unseen environments.\nWe also observed that increasing the augmentation probability of individual RE and RA degrades performance and uniformity (Fig. B), confirming the polarized effect caused by these augmentations in DG ReID.\n\nIn summary, we generally observe the polarized effect across different backbones, loss functions, and augmentation types, and we believe this is a more general phenomenon in DG re-ID. \nFurthermore, the proposed BAU consistently improves various baselines with different backbones and losses (Tab. B) and various augmentation types (Tab. 7 in main paper).\nBased on extensive experiments and analysis, we believe that the polarized effect in our study is a general phenomenon and that we have introduced a simple but general BAU method that can have a significant impact on this field.\n\nRegarding the discrepancy with the results reported in QAConv, it is important to note that QAConv is a DG re-ID method based on local feature matching, which may lead to a different impact of RE compared to the conventional global feature approach used in our experiments. For the feature matching task, RE can improve the model's ability to learn various local correspondences while simulating diverse occlusions [44].\n\n### **Comparisons regarding augmentations used**\nWhile BAU employs a combination of augmentations (CJ, RA, RE), we would like to clarify that recent state-of-the-art methods, such as META [77] and ACL [81], utilize similar augmentations (e.g., AutoAugment and CJ).\n\nTo further investigate the impact of BAU's augmentations on other DG methods, we applied BAU's augmentations to ACL based on the official implementation provided by the authors (https://github.com/peterzpy/ACL-DGReID) in Table C.\nThe results show that the performance of ACL decreases when it utilizes the augmentations used in BAU, which implies that the polarized effect can also occur to other methods.\nFurthermore, BAU shows consistent performance when it utilizes the augmentations used in ACL.\nAdditionally, as demonstrated in Table 7 of the main paper, BAU consistently shows superior performance across various augmentation configurations, including commonly used data augmentations (CJ+RE).\n\nIn summary, BAU effectively mitigates the polarized effect of data augmentations while demonstrating robust performance across various augmentation configurations.\n\n### **Novelty of BAU**\n\nWhile our method builds upon the concepts of alignment and uniformity introduced in [72], we would like to clarify that we extend these ideas from the self-supervised learning to DG re-ID task.\n\nAs gratefully mentioned by the reviewer, we propose additional components tailored to the DG re-ID task, such as the novel weighting strategy and the domain-specific uniformity loss. \nExtensive ablation studies and analyses in the main paper (Tables 5-6, Figures 4-5) demonstrate the effectiveness of these components.\nFurthermore, we provide additional analysis on the weighting strategy in Figure C of the rebuttal PDF, which verifies its effectiveness across various augmentation probabilities.\n\nIn summary, our work provides new insights into the polarized effect of data augmentations in DG re-ID and proposes a simple yet effective solution to mitigate this issue.\nWe believe our findings will have a positive impact on subsequent research in this field, particularly when developing DG re-ID methods based on augmentation techniques, which is a straightforward solution for improving generalization.\n\n### **Computation cost**\nThe weighting strategy computes Jaccard similarity of k-reciprocal NN within a mini-batch, with time complexity $O(N^2 \\log(N))$ for batch size $N$.\nHere we provide computation time comparison between baseline and BAU with the same batch size.\n\n|Method| Time(s)/Iter|\n|-|-|\n|Baseline| 0.312|\n|BAU| 0.412|\n|weight $w$ | 0.086|\n\nConsidering the effectiveness of the weighting strategy (Figure C), we believe that this slight additional cost is reasonable.\n\nWe further address your questions on the alignment loss and the domain-specific loss in the following comments.\n\n---\n\n[a] ArcFace: Additive Angular Margin Loss for Deep Face Recognition, CVPR 2019\n\n[b] Prototypical Contrastive Learning of Unsupervised Representations, ICLR 2021", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
Dear reviewers and chairs,

We sincerely appreciate all reviewers for their time and efforts for reviewing our work.
We are glad that the reviewers found our work  "clear motivation and idea"(Ywj9, 4LRb), "simple and effective"(L6wV, 4LRb), "well presented"(Ywj9, 4LRb), and "sufficient experimental evidence" (Ywj9, fzqs).
We believe our BAU has potential to complement the ongoing advancements in data augmentation techniques, offering opportunities for synergistic combinations to further improve model generalization.

In response to the reviewers' constructive feedback and insightful comments, we have included a 1-page PDF with additional experimental results.

This additional material addresses key concerns and provides further evidence to support our method:

* Figures A and B: Investigate ***Commonality of the polarized effect*** across different backbones, loss functions, and augmentation types. (fzqs, 4LRb)
* Table A and Figure C: Demostrate ***Effectiveness of the alignment loss*** with our proposed weighting strategy. (L6wV, Ywj9, 4LRb)
* Table B: Shows ***Applicability of the proposed method***  on various types of baselines. (L6wV)
* Table C: Validate ***Robustness of BAU*** with different augmentation configurations. (4LRb)

Overall, the attached 1-page PDF includes:

- **Figure A**: Additional investigation of the existence of polarized effects with different types of (a) backbones, (b) loss functions, and (c) augmentation types.

- **Figure B**: Additional analysis of polarized effect with varying augmentation probabilities by individual data augmentation, (a) Random Erasing and (b) RandAugment.

- **Figure C**: An analysis of the weighting strategy for the alignment loss with varying augmentation probabilities.

- **Table A**: A comparison between different strategies of alignment loss.

- **Table B**: A validation of the proposed BAU on various baseline models consisting of an unsupervised approach, different backbones, and loss functions.

- **Table C**: A comparison between recent SoTA method ACL and the proposed BAU with different augmentation configurations.

Once again, we would like to thank all reviewers for their constructive feedback, which helped us strengthen our work.

Best regards,

Authors of submission 16440

Meta-review: 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.
Decision: Accept (poster)