Exploring the Camera Bias of Person Re-identification
Authors: Myungseo Song
Jin-Woo Park
Jong-Seok Lee
Venue: ICLR 2025 Spotlight (2025)
Keywords: Person re-identification
Camera bias
Debiasing
Unsupervised learning
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We investigate the camera bias problem of ReID models, including debiasing effects of feature normalization and risk of camera bias in unsupervised learning.

Abstract: We empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address this issue, but they are largely confined to training domains of the models. We measure the camera bias of ReID models on unseen domains and reveal that camera bias becomes more pronounced under data distribution shifts. As a debiasing method for unseen domain data, we revisit feature normalization on embedding vectors. While the normalization has been used as a straightforward solution, its underlying causes and broader applicability remain unexplored. We analyze why this simple method is effective at reducing bias and show that it can be applied to detailed bias factors such as low-level image properties and body angle. Furthermore, we validate its generalizability across various models and benchmarks, highlighting its potential as a simple yet effective test-time postprocessing method for ReID. In addition, we explore the inherent risk of camera bias in unsupervised learning of ReID models. The unsupervised models remain highly biased towards camera labels even for seen domain data, indicating substantial room for improvement. Based on observations of the negative impact of camera-biased pseudo labels on training, we suggest simple training strategies to mitigate the bias. By applying these strategies to existing unsupervised learning algorithms, we show that significant performance improvements can be achieved with minor modifications.

Published as a conference paper at ICLR 2025
EXPLORING THE CAMERA BIAS OF PERSON RE-IDENTIFICATION
Myungseo Song & Jin- Jin Park
Jong-Seok Lee Yonsei University Scoul, Korea
mAy-I Inc.
Seoul, Korea Imyungseo.sov jin) @may-1... -oc---... io
jong-seok. lee@yonsei
ABSTRACT
We empirically investigate the camera bias of person re-identification (ReID) models. Previously, camera-aware methods have been proposed to address this issue, but they are largely confined to training domains of the models. We measure the camera bias of ReID models on unseen domains and reveal that camera bias becomes more pronounced under data distribution shifts. As debiasing method for unseen domain data, we revisit feature normalization on embedding vectors. While the normalization has been used straightforward solution, its underlying causes and broader applicability remain unexplored. We analyze why this simple method is effective at reducing bias and show that it can be applied detailed bias factors such as low-level image properties and body angle. Furthermore, we validate its generalizability across various models and benchmarks, highlighting its potential as simple yet effective test-time postprocessing method for ReID, In addition, we explore the inherent risk of camera bias in unsupervised learning of RelD models. The unsupervised models remain highly biased towards camera labels even for seen domain data, indicating substantial room for improvement. Based on observations of the negative impact of camera-biased pseudo labels training, we suggest simple training strategies to mitigate the bias. By applying these strategies to existing unsupervised learning algorithms,, show that significant performance improvements can be achieved with minor modifications.
INTRODUCTION
Person re-identification (ReID) process of retrieving images of a query identity from gallery images. With recent advances in deep learning, a wide range of challenging RelD scenarios have been covered, including object occlusion (Miao et al., 2019; Somers et al., 2023), change of appearance (Jin et al., 2022), and infrared images (Wu et al., 2017; Wu & Ye, 2023). In general, the inter-camera sample matching is not trivial since the shared information among images from the same camera can mislead model easily. This phenomenon is known as the problem of camera bias, where samples from the same camera tend to gather closer in the feature space. This increases the false matching between the query- query-gallery samples since the samples of different identities from the same camera can be considered too similar. To address the issue, camera-aware ReID methods (Luo et al., 2020; Wang et al., 2021; Chen et al., 2021; Cho al., 2022; Lee et al., 2023) have been proposed, aiming to learn camera-invariant
camera- invariant representations by leveraging camera labels of samples during training.
However, the previous works on camera bias of ReID models have mainly focused on seen domains of the models, while the camera bias of RelD models unseen domains has been overlooked. We observe that existing RelD models exhibit a large camera bias for unseen domain data. For example, Figure describes the feature distance distributions between samples of camera-aware model (Cho et al., 2022) trained on the Market-1501 (Zheng et al., 2015) dataset, using samples from the MSMT17 (Wei et al., 2018) dataset. Compared the distance distributions of the seen domain samples, the distance distributions of the unseen domain samples are more separable.
In this paper, first investigate the camera bias of existing ReID models data. We observe that, regardless of the model types, there is large camera bias in distribution shifts, and unsupervised models vulnerable to camera bias even seen domains. As straightforward
and unseen domain

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Published as a conference paper at ICLR 2025
Same identity camera
identity same camera Different identity & different camera
Same identiyy & different camera
Density
6.0
0.8
0.8
Distance
Distance (b)
Figure 1: Cosine distance distributions of camera-aware ReID model on (a) the training domain (Market-1500 1501) and (b) the unseen domain (MSMT17). The distances between samples within the same cameras are more skewed to the left when the data distribution is shifted.
debiasing technique for unseen domains, we revisit the normalization method on the embedding features of ReID models. Through comprehensive empirical analysis, we reveal why the feature normalization effectively reduces biases towards camera labels and fine-grained factors such as low-level image properties and body angles, well as demonstrating its general applicability for various RelD models. Additionally, we explore the inherent risk of camera bias in unsupervised learning (USL) ReID models, observing the negative impact of camera-biasee pseudo labels on training. Based on our analysis, we suggest simple training strategies applicable to existing USL algorithms, which significantly improve the performance.
The main contributions of this work are summarized as follows:
We investigate the camera bias of ReID models on unseen domain data, which has been thoroughly studied. We provide comprehensive analysis encompassing various learning methods and model architectures.
We revisit the debiasing effects of normalization on embedding vectors of ReID models. The empirical analysis explains why it is effective for bias mitigation and shows its applicability detailed bias factors and multiple models.
We explore the risk of camera bias inherent in unsupervised learning of ReID models. From this, we show that the performance of existing unsupervised algorithms can effectively enhanced by simple modifications to reduce the risk.
RELATED WORK
In traditional person ReID methods, the convolutional neural networks (CNN) architectures have been popularly adopted with cross-entropy and triplet loss (Zheng et al., 2017; Hermans et al., 2017; Luo et al., 2019; Ye et al., 2021). When identity labels of training data unavailable, the pseudo labels are used instead based on clustering on the extracted features (Fan et al., 2018; Lin et al., 2019; Yu et al., 2019; Zhang et al., 2019; Dai al., 2022). Recently, the transformer backbones (He et al., 2021; Luo et al., 2021b; Chen et al., 2023) and self-supervised pretraining (Fu et al., 2021; 2022; Luo al., 2021b: Chen al., 2023) significantly improve the RelD performance. To enhance the generalization ability of the models, variety of domain generalizable techniques have been also proposed (Dai et al., 2021: Song et al., 2019; Liao & Shao, 2021; Ni al.. 2023; Dou et al., 2023).
However, it has been found that the ReID models are biased towards the camera views of given data. The camera-awan methods have been proposed to alleviate this problem, where camera labels of the samples are utilized in model training as auxiliary information (Luo et al., 2020; Zhuang et al., 2020; Zhang et al., 2021; Wang et al., 2021; Chen al., 2021: Cho et al., 2022: Lee al., 2023). For example, inter-camera contrastive loss is proposed to minimize the variations of the features from different cameras within the same class (Wang et al., 2021: Cho et al., 2022). Zhuang et al. (2020) replace batch normalization layers of model with camera-based batch normalization layers conditional to the camera labels of inputs to reduce the distribution gap. Some other studies (Gu et al., 2020; Luo et al., 2021a) post-process feature by subtracting the mean feature within its camera
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Table I: Camera bias and accuracy of various state-of-the-art ReID models based on clustering results. "SL" and "CA" denote the supervised learning and camera-awar method, respectively. "Bias" and "Accuracy" denote the Normalized Mutual Information (NMI) scores between cluster labels and camera labels, and between cluster labels and identity labels, respectively, in x 100 scale. ISR is trained on external videos and the other models are trained on MSMT17-Train.
MSMTI1-TTain
MSMT T-s
Market- 1501
CURKOSNN
PonanxX
Method
Buckhone
Bias
Accurncy
Bias
Accunscy
Bias
Accurayy
Bias
Accuracy
Bias
Accumsy
(C 2022)
R50
34.7
89.3
17.0
PPLR (Cho et 2022)
R50
31.8
90.3
30.2
$9.0
81.7
15.9
77.4
15.3
82.0
Trand--tD-SSI. (1 20211))
X
X
VIT
29.3
93.1
27.1
92.8
9.7
92.2
7.0
84.2
12.5
88.8
(Dou 2023)
VIT
31.8
90.5
95.8
5.4
57.7
PPLR-CAM (Cho et 2022)
R50
29.3
84,1
TransRelD (Hl et al., 2021)
VIT
26.4
98.3
23.6
94.5
13.6
89.8
3.9
64.7
6.6
92.7
SOLIDER (Chen 2023)
VIT
23.2
98.7
21.3
96.9
96.5
1.6
90.8
2.8
93.8
GGound Truth
21.1
view, but this is performed without justification and is limited to unsupervised domain adaptation task. These previous studies have primarily focused on the bias of the models on the training domain data, while the bias unseen domain data has been neglected. Meanwhile, we call the methods which do not take the camera views into account camera-agnostic methods.
3 QUANTITATIVE ANALYSIS ON CAMERA BIAS
In this section, we quantitatively investigate the camera bias in existing ReID models. The camera bias is the phenomenon where the feature distribution is biased towards the camera labels of the samples, which degrades ReID performance. Many camera-aware methods have been proposed to address this problem. However, the scope of the discussion has been primarily limited to training domain and the camera bias on unseen domains has not been thoroughly explored. We focus on the camera bias of ReID models on unseen domains, examining various types of models including and domain generalizable approaches, with the widely used backbones such as ResNet (He al., 2016) and VIT (Dosovitskiy et al., 2021).
camera-awane aware/agnostic, supervised/ussueeriiete
To measure the bias, we utilize Normalized Mutual Information (NMI) which quantifies the shared information between two clustering results. We extract the features of samples and perform clustering to them using InfoMAP (Rosvall & Bergstrom, 2008). Then, the camera bias is computed by NMI between cluster labels and camera labels of the samples. The accuracy of the clusters are measured by NMI between the cluster labels and the identity labels. The results on MSMT17, Market- 1501, CUHK03-NP (Zhong et al., 201 7a), and PersonX (Sun & Zheng, 2019) are shown Table 1, where the bias of the ground truth (i.e., NMI between the identity labels and the camera labels) indicates the inherent imbalance in a dataset. All models except ISR (Dou et al., 2023) are trained on MSMT17, hence the other datasets are unseen domains for them. For ISR, all datasets are unseen domains.
We make two notable observations from the results. First, the existing ReID models have a large camera bias the unseen domains, regardless of their training setups or backbones. Second, the unsupervised models have large camera bias on the seen domain, even on their training data. These imply that debiasing methods for unseen domains are needed in general, and there is room for performance improvement of unsupervised methods by reducing the camera bias during training. Relatively, the recent supervised models exhibit less debiased results on the training domain.
UNDERSTANDING CAMERA BIAS AND FEATURE NORMALIZATION
4.1 CAMERA-SPECIHIC FEATURE NORMALIZATION
In Section 3, we observed that the ReID models have large camera bias unseen domains. As a straightforward debiasing method, we introduce camera-specific feature normalization which postprocesses embedding vectors leveraging camera labels at test time. It is performed as follows.
Suppose that a test dataset = {xx) . 31 (I2, y2), ... (ÆN, (xN.vx)) with N samples is given, where 2; and y, denote the image and camera label of each sample, respectively. A pretrained encoder
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Dimenson Camera sensitive
Camera insensitive
Variance
10204
Similarity
22-
22
1000
18
6.
de 286 291
de
dic
20 26
6 de vie aa selected dimensions
Sorted Index
selected dimensions
(b)
(c)
Figure 2: Analysis on the 384-dimensional embedding space of RelD model. We measure the similarity of displacement vectors and mAP results increasing the number of feature dimensions following different orders. (a) Variance of each dimension of camera mean features. (b) Cosine similarity of displacement vectors between samples of the same identities from different cameras along selected dimensions. (c) Result of camera-specific feature centering for selected dimensions.
Je is used to extract embedding features F = (f1. f2, fNN) where f. fa()).. We split F into M subsets F2. F....... ..... and FM depending on the camera labels, where the number of cameras is denoted by M. Then, the mean and standard deviation vectors for each camera, m. and oe computed as follows:
m.
E. and o.
I |ia fieFo
(f. m.) o (f: me),
(I)
f.EF.
where @ denotes the element-wise multiplication. The camera-specific feature normalization on with the camera label y; given by:
f. ft my
(2)
This operation has been used as modified forms camera mean subtraction (Gu et al., 2020; Luo et al., 2021a) and camera-specific batch normalization (Zhuang al., 2020). In Zhuang al. (2020), the normalization is followed by an affine transformation learned during training. However, how does the simple camera-specifie feature normalization have debiasing effect? We revisit the camera-specific feature normalization by empirically analyzing why it mitigates the camera bias and demonstrating its generalizability through comprehensive experiments.
4.2 ANALYSIS ON FEATURE SPACE
We dive deeply into the feature space of RelD model (Luo et al., 2021b) trained on MSMT17 using CUHK03-NP samples, to understand why the normalization can play a role of debiasing.
Sensitivity to camera variations differs across dimensions We first find that the sensitivity of each dimension of the feature space to camera variations quite different from each other. We compute mean features of each camera view and present the element wise variances of the mean features in the descending order in Figure 2(a). It shown that some dimensions have a relatively large variation, which might be largely related to the camera bias of the model.
Movements of features due to camera variations We indirectly investigate features, movements due camera changes using the identity labels and camera labels of the samples. We obtain displacement vectors from feature pairs of two different cameras with the same identities (details in Appendix I. and compute their average cosine similarity in selected dimensions, with increasing the number of selected dimensions. Three selecting orders are used: (1) "Dimension index" follows the original index order of the dimensions, (2) "Camera sensitive" follows the descending order the element- wise variances of the camera means, and (3) "Camera insensitive" follows the reverse order of (2). From Figure 2(b), we observe that the similarities of the displacement vectors in the camera- sensitive dimensions relatively large. In other words, the features tend move consistently in the
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Brgfonss
Swapned
CHEEAS:
AND 5rgf01555
Starrees
Certost
Are
31
37
0.55
Similarits
30
.6
645-
mAp
MAP
29-
0.35
10
50
100
10
50
100
Level
# group (b)
# group
(a)
(c)
Figure 3: Analysis on low-level properties, (a) Cosine similarity of displacement vectors by image transformations. (b) Property group-specific feature normalization. The dashed line indicates the performance without normalization. (c) (Property group, camera)-spectine feature normalization. The dashed line indicates the performance with camera-specific (and property-agnostic normalization.
camera- samera-sensitive dimensions depending on camera variation, implying that the effect of a camera change appears translation these embedding dimensions.
Sensitive dimensions dominate debiasing effects Then, can we debias the features by subtracting camera mean features for those sensitive dimensions? find out, we apply a camera-specific centering on selected dimensions in Figure 2(c). Note that there is clear difference in the improve- ment rate ReID performance depending on the selecting order. The performance gains are actually dominated in the samera-sensitive dimensions. For example, centering 13%) of higher variances achieves approximately a half of the total gains, while centering on top-50 dimensions of lower variances shows almost no gain. For the low-variance dimensions, half of the total gains requires centering of as many as 350 dimensions (about 91 1)). Similar results are obtained for other models in Appendix B.2.
top-50 dimensions (about
4.3 ANALYSIS ON DETAILED BIAS FACTORS
We explore the feature normalization for detailed bias factors of RelD models, including image properties and body angle of images. The model (Luo et al., 2021b) trained on MSMT17 is used.
Movements of features due to image transformations Given the fine-grained nature of person RelD, the camera bias of a model might be closely related to the difference in low-level image properties between cameras. Here, we analyze the changes of features due to image transformations applied samples from CUHK03-NP, using eight low-level transformation functions with four levels of transformation strength as shown in Figure 10. The feature of the i-th image and the feature of its
transformed image at level kt are denoted by f. (0) and f(( respectively. For example, for blurring function, f. (4) denotes the feature when the i-th image is most strongly blurred. Then, compute the average cosine similarity between displacement vectors of the features after applying a transformation to the images for each level At which is given by EE [Simm( (k) f: (k-1) fj (k) -f, (k-1))) The result is shown in Figure 3(a). We observe that, for certain transformations such as decreasing brightness, the displacement vector (f; (f(()) - (k-1)) due to the transformation is similar across
different images to some extent, which is analogous to the effect of camera variations.
Normalization for image properties Then, can we reduce biases of the model towards the low- level properties by utilizing the feature normalization? To find out, we calculate the brightness, sharpness, contrast, and area of all samples, as visualized in Figure 11. Note that all samples in the dataset have almost same contrast values. We divide the samples into N groups of equal size for each property. For example, when dividing the samples into N = 2 groups based on the brightness, we use the median brightness value as the threshold for group assignment. Here, a small but meaningful correlation between these group labels and camera labels is observed as shown in Figure 12. Then, we perform group-specific feature normalization on the features using the property group labels. As presented in Figure 3(b), the normalization on the features based on the property groups is effective for brightness, sharpness, and area. does not work for contrast since all contrast values are almost equal. In addition, subdivide each property group into multiple groups based on the camera labels
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Table 2: Feature normalization for body angle. "All" includes images of front, back, and side angles.
All
Front-only
Side-only
Normalization
mAP
R1
mAP
R1
mAP
RI
68.3
84.8
84.3
83.3
Anglo-specific
75.0
81.1
Camera specillc
76.2
84.1
91.1
91.5
89.4
91.3
(Angle, Camera) specifie
80.5
87.2
and present the result of group-specific normalization with the subdivided group labels in Figure 3()). Interestingly, the (property group, camera) normalization outperforms the camera-specific normalization for proper N values. For example, compared to the camera-speciffs normalization, (area group, camera)-specifer normalization exhibits about 1.5 mAP improvements. This implies that further considerations of other bias types of Rell models along with the camera bias are needed. Experimental details and additional results are provided in Appendix C.
Normalization for body angle It has been shown that RelD models have a bias towards the body angle of an image (Sun & Zheng, 2019). This bias is likely to be closely related with the camera bias, since the distribution of body angles would be different for cach camera orientation. Then, can we reduce the bias of the model towards the body angle by using the feature normalization? To find out, we define three body angle classes (front, back, and side) and construct four angle-labeled datasets from Market- 501, including front-only, side-only, back-only, and all-angle dataset. Then, we perform an angle-specific feature normalization on the features using the angle labels, well as (angle, camera)-specioss specific normalization works and the performance is further improved by using the camera labels together. Details of the datasets are described in Appendix D.
normalization like previous paragraph. As shown in Table 2, the angle-
In summary, we observed consistent feature movements due to image transformations and confirmed the applicability of the feature normalization to detailed bias factors such as low-level image prop- erties and body angles. It is encouraging that considering both camera labels and other factors in normalization can achieve performance beyond only considering the camera labels, highlighting the need for further research into biases of RelD models beyond the camera bias. The normalization methods could serve as easy tool for such exploration.
4.4 MORE EMPIRICAL RESULTS
Generalizability We present the evaluation results of the camera-specific feature normalization on multiple ReID models in Table 3. The mean average precision (mAP) and cumulative matching characteristics (CMC) Rank- (R1) are used for evaluation. The NMI scores of clustering results also reported as in Section 3. Note that ISR and PAT (Ni et al., 2023) are domain generalized methods. The feature normalization significantly improves the performance of all models the unseen domain (white background in the table), regardless of training methods or backbones architectures. For example, on Market-1501. CC (Dai et al., 2022) exhibits about 7.5% improvement in mAP and about 5.9% reduction in camera bias, and TransRelD (He et al., 2021) shows about 9.4% improvement in mAP and about 2.7% reduction camera bias. For the seen domain, the camera- agnostic unsupervised models show slight improvement (gray background), while the camera-awe aware or supervised models exhibit no improvement (red background). It likely because the camera bias of these models is already relatively small on the seen domain, e.g., SOLIDER (Chen et al., 2023) has an almost identical bias value to the ground truth. The normalization results of Figure 1(b) are shown in Figure 4, where the less separable distributions are observed. The feature visualization result is illustrated in Figure 15.
Ablation study The camera-specife feature normalization consists of (1) camera-specific, (2) mean centering, and (3) scaling by standard deviation on the features. We investigate the effectiveness of each component for CUHK03-NP in Table 4, using TransReID-SSL. (Luo et al., 2021b) trained on MSMT17. ZCA whitening is also evaluated to check the effectiveness of rotation related to covariance across feature dimensions. There are some gains from the entire transforms, but the camera-specific transforms outperform them. It is observed that the camera-specifie mean centering has a dominant effect and the scaling operation provides a small but additional gain. The rotation by the ZCA whitening does not exhibit definite gains compared to the normalization.

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Published as a conference paper at ICLR 2025
Table 3: Evaluation results of the camera-specific feature normalization for various ReID models. The numbers denote the performance before/after normalization. "SL" and "CA" denote the supervised learning and camera-aware methods, respectively. "f" indicates transformer backbones. "*" indicates our reproduced results. ISR is trained on external videos and the others are trained on MSMT17.
(a) RelD performance
Market 1501
MSMT17
CUIIKO NP
PersonX
Mechod
CA
mAP
mAP
R1
mAP
mAP
SPCL (Ge 2020)
16.0
37.17
20.3
42.4/44.4
6.179.1
5.781
30.4310
41.2/53.7
CC (1ai at. 2022)
22.5 30.0
47.3/ 56.4
29.8/ 32.2
57.1 60.4
8.4/1355
8.3/131
24.7/36.8
62.4/62.1
PPLR (Cho al., 2022)
25.2 31.
53.7 61.6
30.6/
59.5 62.4
10.1 14.
9
30.7 39.4
57.3 68.2
TransRel-SSS: et 2021h)
53.6/22.3
K335
75.0/77.3 53.5
23.2/35.7
254/344
45.4
ISR" (Don et 2023)
71.9
87.00 87.88
32.5 /3.2
58.8 60.8
38.6/42.3
37.17 40.5
66.4/ 70.2
83.1 M5.3
CAP* (Wang et al,, 2021)
30.8 36.6
58.9/65.3
36.3/ 36.6
67.5
15.5/17.9
16.37 18.7
36,9 45.0
61.6 72.7
ICE-CAM (Chen al., 2021)
25.91
$3.4/
37.8 37..
66.9167.5
12.2/17.2
11.9/ 16.4
26.2/ 39.8
52.2 66.7
PPLR-CAM (Cho al,, 2022)
28.4/34.5
58.3 65.1
42.2/41.3
73.2/73.3
12.0/16. /1.2
12.0/ 15.9
31,00
57.4/ 68.6
CAJ (Chen et 2025)
30.6 36.9
61.3/ 68.1
44.3/42.8
7
14.1 /1.11
14.5/ 19.1
32.5 40.9
59.5/ /9.5/70.0
PAT" (Ni ot al,, 2023)
43.8/52.9
70.4/ 76.8
54.8/55 54.1
78.0/ 78.3
24.5/ 29.8
29.2/29.9 29.9
50.0 59.8
72.8 M004
SOLIDER! (Chen 2025)
72.4/79.2
89.0
77.1/770
96.7 9906
55.7
53.81 59.1
55.4/ 63.4
TransRelD)
2021)
43.1/52.5
69.5/ 76.1
67.8/666
85.4/85.0
20.9 /3.5
57.7
N2.8
(b) Clustering result
Method
St. CA
Market- 501
MSMT17
CUHKO1-NP Accuracy
PersonX
Acuaracy
Aucuracy
Avcuncy
SPCL (Ge et 2020)
22.5/15.1
75.07 79.5
34.7 32.2
S4.0784.5
18.2/447
71.27 74.0
22,07 13.2
74.3 2016
CC (Dai et 2022)
17.1 11.2
$1.0 84.7
32.5/29.7
$8.0/89.0
17.6 /7.6/10.8
74.6/ 78.4
20.6 /9.5
78.9 /8..1
PPLR (Cho ol., 2002)
81.7/85.2
30.2/27.0
89.0/89.9
15.9/10
77.47 90.5
15.3/63
TraaskelD-SSL. (Luo et 2021b)
9.7722
92.2/ 94.3
27.1 25.4
92.8 92.8/93.6
7.0/4.0
84.21 86.9
12.5
59.8 993.5
ISR (Don et al., 2023)
X
9.7194
95.8/ 96.0
30.3/ 29.6
80.4/89.9
5.4/4.5
87.2188.9
6.1/45
94.9 95.99
CAP* (Wang et 2021)
12.3/8.2
86.9
25.5/24.8
90.1790.1
8.066.5
78.87 80.6
9.9/5.2
S6.0 89.6
ICE-CAM" (Chan
13.//91
N2.9
26.8/ 25.4
90.6
12.7 7.3
71.57 80.7
7.4
78.3 86.1
PPLR- (Cho et 2022)
14.2
84.1 87.2
26.7/ 25.5
92.4/12.5
10.0
78.47
14,6 6.4
$1.8 88.0
CAJ (Ches et all.ll22222 2024)
12.2/8.9
82.1 84.2
25.2//24.3
92,7/99.4
11.6/8.3
80.81 83.3
12.2/5.7
83.3 /8800
(Ni ot al. 2023)
108/9.1
24.0
94.3
6.7444
7.7/35
SOLIDER (Chen
2023)
7.3669
96.5 97.5
21.3/21.4
96.9//9.9
1.6/1.6
92.8
2.8/
/9.8
TransRelD (He et 2021)
1366 10.9
89.8 /9.2
23.6/22.6
94.5795.2
3.9/366
84.7/ 86.8
6.6/33
92.7 /95.1
Ground Trush
6.4
19.2
0.1
0.0
Same same cam. Same afferent cam.
mitment same Different difterent cam.
Table 4: Ablation study of feature normalization.
Density
Entree
Camera-spaciiic
Method
mAP
RI
2-
Baseline
27.2
25,4
27.2
25.4
1-
+ Centering
27.8
25.8
34.7
33.0
+ Scaling
27.7
25.6
27.2
25.1
0.0
0.4
0.8
1.2
+ Centering Scaling
28.8
26.6
35.7
34.4
Distance
+ 7CA whitening
18.7
19.1
30.1
35.1
Figure 4: Normalization result of Figure 1(b).
Image volume for computing normalization parameters We explore the impact of the number of samples used to compute the statistics for normalization in Figure 5. Equal numbers of samples are randomly sampled from each camera in Market-1501. TransReID-SSY trained on MSMT17 is used in this experiments. The performance is degraded when using too few samples per camera (e.g., five samples). Interestingly, using more than 25 samples per camera leads to positive effects and the gains start to be saturated over 100 samples. This suggests that the number of samples needed to represent camera features is not very large.
Combination with other postprocessing methods We test whether the camera-specific feature normalization is still effective when used in conjunction with other postprocessing algorithms, using TransRelD-Se trained on MSMT17. As shown in Table 5, the normalization brings about 9% gains in mAP for all methods. In other words, the problem of camera bias remains after applying the conventional postprocessing methods. Note that unlike other methods resulting in decreases in R5 and R 110, the normalization consistently improves all metrics.
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M/ normalisation 2ON0 sampics
Table 5: Combination results with other feature postprocessing methods on Market- 1501.
60
normal
544
mAP
40
Pusiprocessing
mAP
R10
20
Nome
53.6
78.1
89.2
92.3
Normalization
92.8
95.5
7/
DBA (Gordo al,, 2017)
59.2
77.9
88.4
91.8
-Normalization
68.6
84.2
92.7
95.1
#
10
Sample per camera
25
50
100
300
1500
AQE (Chum al., 2007)
61.1
78.9
87.5
90.7
Normalization
70,6
85,1
91.8
94,3
Figure 5: Results based on the number of sam-
Reranking (Zhong al., 2017b)
67.7
78.4
85.2
88.6
Nommlization
76.3
84.7
90.7
93.0
ples used to calculate normalization parameters.
RISK OF CAMERA BIAS IN UNSUPERVISED LEARNING
5.1 RISK OF BIASED PSEUDO LABELS
In Section 3, we observed that the ReID models learned in unsupervised manners have a large camera bias even their training data. We argue that the existing USL algorithms have two limitations introducing the camera bias into the models. First, the pseudo labels of training data are biased towards the camera labels. In USL. a model is supervised by the pseudo labels of the training samples which are usually generated by clustering of the features extracted by the model. However, as we have seen, the clustering result is already camera-biased hence using them for training would make the model dependent on the camera-related information. Second, cumera-biased clusters with few cameras are used in training without sufficient consideration. For example, consider a cluster only consisting of samples from one camera. Since most of the samples of this cluster may share similar camera- related information (e.g., background), utilizing them as positive training samples can lead the model to pay more attention the common camera-related information. Also, the samples in that cluster were likely grouped together incorrectly due to the shared camera information, which is expected to be more common in the early stage of model training.
5.2 TOY EXAMPLE RESULTS
We investigate the risks of biased training data toward cameras using toy examples. ResNet50 models are trained on the toy datasets using the cluster contrastive loss (Dai al., 2022) in the experiments.
Figure 6(a) compares the training results with the different levels of camera bias and accuracy of pseudo labels for the same training samples. We constructed dataset of 7500 samples by randomly selecting 500 identities from Market-1501, where each identity has 5 samples per camera with 3 cameras. To generate pseudo labels with varying degrees of camera bias at the same accuracy, certain proportion of identities were divided into three equally-sized clusters for each identity based either on camera labels ("Camera") or random selection ( ("Random")) Five splitting ratios of 0%, 25%, 50%, 75%, and 100% were used. For example, the pseudo labels generated by splitting 50% of the identities consist of 250 original clusters and 750 split clusters, totaling 1000 clusters. The bias of the pseudo labels is measured by calculating the mean entropy of the camera labels within each cluster (Lee et al., 2023). It is observed that, at the same pseudo label accuracy, models trained with "Camera" consistently perform worse than those trained with "Random". Moreover, "Random" with 91.9% accuracy outperforms "Camera" with 93.8% accuracy, despite having lower accuracy. These results suggest that greater camera bias of pseudo labels has a detrimental effect on model training, and pseudo labels with lower accuracy but less camera bias can provide more benefits than those with higher accuracy but greater camera bias.
Figure 6(b) illustrates the impact of camera diversity of training data, using ground truth labels. We constructed five datasets of 11821 samples of 1041 identities from MSMT17, where the maximum numbers of cameras per identity are different. As expected, the model performance declines as the maximum number of cameras decreases. Notably, a significant performance drop is observed when the model is trained with samples from only a single camera for each identity. This suggests that using single-camera clusters for training can degrade the model performance. In addition, the influence of clustering parameter on the camera bias is investigated in Appendix I.
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Random
Camera
40
50-
143
30
140
6.7
40
mAP
20-10
0.40
20
10
9.5
92
94
96 98
100
4
Accuracy of pseudo labels (a)
Maximum number of cameras per identity
(b)
Figure 6: Risk of biased clusters. (a) Training results with varying pseudo label qualities for the same training samples. Two pseudo label generation methods, "Random" and "Camera", used. The colored numbers denote the average camera entropy of the clusters. (b) Training results with varying maximum number of cameras per identity for the same number of samples.
Algorithm Unsupervised learning algorithm for Person ReID with simple modificaitons
Require:
Initialized backbone encoder fo training samples with camera labels
for in [1, mum, epochs] do
Extract features
by fo-
(1) Deblased pscudo labeling:
Transform F to F by applying the camern specific feature nommlization.
Generate pseudo lahels by clustering F.
(2) Discarding blased clusters:
Collect the images belong to the clusters of single camera B.
B. Prepare for tmining iterations (e.g. initialization of feature memory).
Reconstruct training images by X"
for in [1, num, litrrttios]] do
Sample mini hatch
reconstracted X²
Compute loss (e.e. contrastive loss).
Update encoder fo.
Update auxiliary modules (e.g. update of Teature memory).
end for
end for
5.3 SIMPLE STRATEGIES FOR DEBIASED UNSUPERVISED LEARNING
To reduce the explored risk of camera bias in unsupervised learning, we present two simple training strategies applicable to existing USL algorithms; (1) debiased pseudo labeling: clustering on the debiased features computed by Equation 2 instead of the original features when generating pseudo labels, and (2) discarding biased clusters: discarding the single-camera clusters in training data. We present an example of applying the proposed strategies to unsupervised learning in Algorithm 1. With these minor modifications, we observe significant performance improvements in next section.
5.4 EMPIRICAL RESULTS
validate the suggested training strategies on the SOTA camera-agnostic methods, CC and PPLR, and the SOTA camera-aware method, PPLR-CAM. A vehicle RelD dataset, VeRi-776 (Liu et al., 2016), is additionally used and the person and vehicle images are resized to 384 x 128 and 256 256, respectively, following the setup of PPLR. The models trained on a H100 GPU with batch size 256 and 100 training epochs, with DBSCAN (Ester et al., 1996) to obtain pseudo labels. Our strategies effectively improves all methods presented in Table 6. In particular, outstanding performance gains are obtained on the challenging benchmark, MSMT17, e.g., 19.3% mAP increase for CC. The gains for PPLR-CAM are relatively small, which is likely because it uses camera-aware loss function. In addition, the number of discarded training samples by our strategy is discussed in Appendix H.
Ablation study Table(a) of Figure 7 investigates the individual effect of our strategies, using CC. Both of the suggested strategies contribute to the performance improvements by reducing the camera bias. The ratio of the single-camera clusters and clustering accuracy during training illustrated in the plot Figure 7. observed that the baseline has unusually high single-camera cluster rates (from about 80% about 35%), which is effectively mitigated by the proposed methods.

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Published as a conference paper at ICLR 2025
Table 6: Results of modifying the existing USL algorithms based on our training strategies. "g" indicates our reproduced result with the official code.
Market- 1501
MSMT17
VeRi-776
Method
R1
mAP
R1O
mAP
Camera agmastic unssperviied
SPCL (Go et al., 2020)
73.1
88.1
95.1
97.0
19.1
42.3
55.6
61.2
36.9
79.9
86.8
89.9
ICE (Chen et al, 2021)
79.5
92.0
97.0
98.1
29.8
59.0
71.7
77.0
PPLR (Cho et al,, 2022)
81.5
92.8
97.1
98.1
31.4
61.1
73.4
77.8
41.6
85.6
91.1
93.4
CC (Dai et al., 2022)
83.0
92.9
97.2
98.0
33.0
62.0
71.8
76.7
40.8
86.2
90.5
92.8
Comenn-aware inzpeerised
(Wang al., 2021)
91.4
96.3
36.9
78.0
81.4
ICE CAM (Chen al.,
93.8
97.6
98.4
38.9
80.5
84.4
PPLR CAM (Cho et 2022)
84.4
94.3
97.8
988.6
42.2
73.3
83.5
86.5
43.5
88.3
92.7
94.4
PPLR* (Cho et al., 2022)
77.4
89.6
96.1
97.4
27.2
55.7
67.1
71.8
41.5
85.6
91.4
93.2
PPLR* (Cho et al., 2022) + Ours
84.6
93.9
97.8
98.6
40.7
71.4
82.3
85.4
43.2
86.7
91.7
93.7
PPLR-CAM (Cho et al., 2022)
84,1
94.0
97.7
98.6
40.7
71.8
82.6
85.7
43.3
88.1
92.2
94.2
PPLR-CAM* (Cho et al,, 2022) Ours
93.8
98.1
44.4
75.8
84.9
87.7
43.7
88.2
CC* (Dai al., 2022)
82.6
91.8
96.7
97.8
29.8
68.5
72.8
38.3
79.8
83.9
86.9
OC* (Dai al., 2022) Ours
85.2
93.5
49.1
76.5
85.6
88.3
89,8
Baseline
Discarding blased
Ratio of single-camera -camera clusters
Method
mAP
R1
R5
R10
Bias
0.60
Baseline
29.8
57.1
68.5
72.8
32.5
(a) Ablation study
Debiased pocudo labeling Discarding biased clusters
85.1
26.5
45.4
73.7
83.4
86.6
25.4
Both of (1) and (2)
49.1
76.5
85.6
88.3
23,8
(b) Claster-wei weighted loss
0.90
CD loss (Lee al., 2023)
40.7
69.4
80.2
83.8
25.1
Binary weighting
40.1
69.3
79.9
83.3
25.9
0.85
Ground Truth
19.2
6.80
Accuracy of clusters
20
40
60
30
100
Training epoch
Figure 7: Results of our training strategies for debiased unsupervised learning on MSMT17.
Comparison to weighted loss We experiment with weighted loss methods related to discarding biased clusters in Table(b) in Figure 7. The camera diversity (CD) loss (Lee et al., 2023) weights sample proportionally to the diversity of cameras within the cluster it belongs to, with O-weight for single-camera clusters. The binary weighting is a variant of it, where we assign 1 1-weight to clusters of more than one camera. It is observed that the performance improvement of the CD loss is dominated by the O-weight setting for single-camera clusters. In our opinion, the CD loss has a side effect of randomizing the mini-batch size or learning rate, since the effective number of samples involved in a model update is randomly changed at each training iteration. Discarding the biased clusters in training a simpler method free from such drawbacks, outperforming the CD loss.
CONCLUSION
We revisited the debiasing effects of normalization on embedding vectors of RelD models and explored the risk of camera bias inherent in unsupervised learning for ReID models. We found that the existing RelD models are biased towards camera labels on unseen domain, and the unsupervised models even have a large camera bias their training data. We analyzed why the camera-speeific feature normalization has debiasing effects and explored its potential and applicability for ReID tasks in comprehensive empirical studies. was observed that, for camera variation, the sensitivity of each feature dimension is quite different and features tend to move consistently in sensitive dimen- sions. Then, was shown that the feature normalization is a simple but effective bias elimination method for ReID models in general, including biases towards low-level properties and body angle. Also, we empirically showed the detrimental effects of biased pseudo labels using toy examples and achieved significant performance improvements with simple modifications to the existing unsuper- vised algorithms. We hope that the insights from this work will serve as an insightful foundation for researching biases of RelD models and developing debiasing techniques for RelD models.
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A STATISTICS OF BENCHMARKS
Table 7: Statisties of datasets used in experiments. In PersonX, each identity has 36 images for each camera.
Datavel
images
camneras
CHU K03-NP
1,467
14,097
2
Indoor
Market 1501
1,501
32,668
6
Outdoor
MSMT17
4,101
126,441
15
Indonrioutdoor
PersonX
1,266
273,465
6
Synthetic
Outdoor
B ADDITIONAL DISCUSSIONS ON FEATURE SPACE
B. EXPERIMENTAL DETAILS
Here, we explain how we calculate the displacement vectors from feature pairs of two different cameras with the same identity in Section 4.2. Suppose labeled dataset is given. For the i-th identity, we denote its k-th image in the j-th camera by 2k
(f.j), We denote the number of images of the i-th
identity from the j-th camera by ((i.)),ee. i.e., the i-th identity has ((5)) images from the j-th camera. For pretrained encoder fo. the feature of æ an average representation i-th identity the j-th camera, g(15), as follows:
(((()) given by fk ((())) fa (xx Then, we compute
N(6.)
8 (6.)
[&
(1d)
((
The displacement vector of the i-th identity between the j-th camera and I-th camera, d' is given by
-= (1,10 ((.5)
The displacement vector d can be thought as the motion of the feature due to the camera change from the j-th camera to the /-th camera. The average cosine similarity between the displacement vectors is computed by
Em. [ii dd d)
(5)
where (p, q) and (m, n) denote a pair of different cameras and a pair of different identities, respec- tively, and Sim denotes the cosine similarity function.
B.2 ADDITIONAL RESULTS
We additionally analyze the feature space of other models, PPLR-CAN and SOLIDER, following the experimental setup of Section 4.2. The results presented in Figures 8 and The tendencies previously discussed for TransRelD-SSL. are also observed for these models.
Dimension index
Camera sensitive
Camera insensitive
18007
Variance
38905
Similarity
0.22
mAP
013
10585
005
Sorted Index
selected dimensions
selected dimensions
(a)
(b)
(c)
Figure 8: Analysis on the 2048-dimensional embedding space of PPLR-CAM (Cho et al., 2022). (a)
Variance of each dimension of camera mean features. (b) Cosine similarity of displacement vectors between feature pairs of the same identity from different cameras for selected dimensions. (c) Result of camera-specific centering the features for selected dimensions.
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Dimenson Index
Camera sensitvee
Camera insensitive
4220
4375
Variance
4325-
Similarity
1230
0.25
MAP
0.39
2.75
2.75
2590-
2330
52-
332
168
212
280
420
1868
6do sin
Sorted
Index
selected dimensions
selected dimensions
(b)
(c)
Figure 9: Analysis on the 1024-dimensional embedding space of SOLIDER (Chen et al., 2023), (a) Variance of each dimension of camera mean features. (b) Cosine similarity of displacement vectors between feature pairs of the same identity from different cameras for selected dimensions. (c) Result of camera-specific centering on the features for selected dimensions.
C ADDITIONAL DISCUSSIONS ON LOW-LEVEL IMAGE PROPERTIES
C.1 EXPERIMENTAL DETAILS
Brightnest dark
Brightness light
Original
k=
k-2
k-3
k-4
Original
1=
k-2
k-3
kid
Blur
Sharp
Original
Original
Contrast_
Contrast_down
Original
k=1
k=2
k=3
k-4
Original
k=
k-2
k-3
k=4
Saturation up
Saturation down
Original
k-1
k-2
k-3
k-6
Original
k-1
k-2
k-3
k-1
Figure 10: Examples of applying the transformation functions to image with four strength levels.
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Brightness
Sharpness
Contrast
Area
200
Figure 11: Statistics of low-level properties of images used in our experiments on each low-level property. Note that all images have the same contrast value.
0.08
0.06
NMI
0.04
0.02
0.00
2
5
10
50
100
group
Figure 12: The Normalized Mutual Information (NMI) scores between property group labels and camera labels for each property. weak correlation between them is observed, except the contrast.
C.2 ADDITIONAL RESULTS
Baneres..okk Ny
0.60
0.80
0.55
0,75
imilarity
0.50
imillrrity
0,70
0.65
0.60
0.50
0.45
0.20
0.40
4
3
Level
Level
3
SULBESUUE
01gttm dec
+ ou
1.0
1.2
0.9
Similarity
0.8
Similarity
0.6-
0.6
0.5
0.4
0.4
0.3
0.2
0.2
0.1
(1.2)
(2.3) Level
(3,4)
(1.2)
(3.4)
Level
(c)
(d)
Figure 13: Cosine similarity of displacement vectors of the features due to low-level transformations.
Here, we analyze the movements of the features due to low-level image transformations in multiple aspects. In the experiments, we define four levels of transformation strength for several low-level transformation functions, shown in Figure 10. For transformation function, we denote the feature of the i-th image and the feature of its transformed image level & by f. (0) and f; (k) respectively.
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Published as a conference paper at ICLR 2025
For example, for the blurring function, denotes the feature when the i-th image is most strongly blurred. Then, we denote the displacement vector of the feature after applying the transformation to the images for a level k by d, (k) f: (k) f. (k-1), We also denote the average displacement vector in the level & by m = E. d (A)). We investigate the tendency of the movements of the features by computing the following cosine similarity between the displacement vectors:
(a) Ex [Sim(d, (8) ,d
d13 (R))): How similar are the movements of features to each other under a transformation?
(b) [Si (k), .m )]: How similar are the movements of features to the average movement under transformation?
(c) [Si[Sim (6) d, (x+1)))] How similar are the movements of features to their previous motion when stronger transformation is applied?
(d) E.y [s ()++ How consistent is the average movement when a stronger transformation is applied?
The results are presented Figure 13. It can be observed that the motions of the features due to specific low-level transformations, e.g., "Contrast_dowee are similar each other overall.
D EXPERIMENTAL DETAILS ON BODY ANGLE
Front
Back
Side
Figure 14: Templates of three body angle classes defined in our experiment. The blue and orange points denote the right and left body parts, respectively.
Table 8: Testset statistics of the experiments on body angle. The number of images in each case is shown.
Class
CAM 1
CAM 2
CAM 3
CAM4
CAM
CAM 6
All
Front
406
557
639
101
91
243
2,037
Back
410
235
639
121
95
191
1,691
Side
120
63
222
102
44
108
659
Total
936
855
1,500
324
230
542
4.387
We define three body angle classes of front, back, and side, and construct test set which is subset of Market- 1501. As shown in Figure 14, we define a template of body keypoints to each class. To obtain the labels of test images, et al., 2019), and classify the images through a template- based nearest neighbor classification. The statistics of the constructed dataset are presented in Table 8.
extract the body keypoints of the images using MediaPipe (Lugaresi
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Published as a conference paper at ICLR 2025
E INFLUENCE OF CAMERA BALANCE IN TRAINING DATA
Table 9: Evaluation results of the camera-specific normalization for CC trained on PersonX with the ground truth labels.
Market- 1501
MSMT17
CUHK03-NP
PersonX
mAP
R1
mAP
R1
mAP
R1
mAP
R1
12.7 18.8
31.6/ 39.3
1.2 2.3
4.0 6.6
4.5 /
7.0
4.4 6.5
87.8 / 88.8
95.4 95.9
In the widely used training datasets, using Market- 1501, MSMT17 and CUHK03-NP, the number of samples of an identity varies for each camera view. other words, there an inherent camera imbalance in these datasets, which may induce the camera bias into the model during training. Then, this imbalance is corrected, would the camera bias be resolved? To find out, CC is trained on PersonX with ground truth labels, where all identities have the same number of samples in each camera. Table 9 presents the evaluation results of the camera-specific normalization on the model for several benchmarks. It is observed that the effect of debiasing is still definite, suggesting that the camera bias still exists even when there is no camera imbalance in the training data. In other words, more effort is required for debiasing beyond balancing the training dataset.
F FEATURE VISUALIZATION RESULT
Original features
Normalized features
Figure 15: The t-SNE result of features of PPLR-CAM trained on MSMT17 using samples from M50ket-1501. Different colors are used for each camera.
Figure 15 presents the t-SNE result of features of PPLR-CAM trained on the MSMT dataset using samples from the Market dataset. It is observed that the features from the same camera tend to cluster more than the features from the different cameras in the left plot. This camera bias is effectively mitigated by the normalization as shown in the right plot.
G ADDITIONAL RESULT OF CAMERA-SPECIEE NORMALIZATION
Table 10: Evaluation result of SPCL trained in an unsupervised domain adaptive manner, with Market- 1501 and MSMT17 the source domain and target domain, respectively. The numbers denote the performance before/after the camera-specific normalization.
Market-155
MSMT17
CUHK03-NP
PersonX
mAP
86.8 86.1
93.9 26.8
56.1
13.9/189
13.3
45.4
59.2/
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Published as a conference paper at ICLR 2025
H
NUMBER OF DISCARDED TRAINING SAMPLES BY OUR USL STRATEGY
Table 11: The proportion of discarded training samples by our training strategy.
Epoch
0
20
40
60
80
100
Discarded samples
63.5%
5.8%
3.0%
2.6%
2.8%
2.8%
Discarding biased clusters in Section 5.3 reduces the effective number of training samples. Table 11 presents the ratio of the discarded samples (i.e., the samples of single-camera clusters) during training of CC with training strategy. We observe drastic discarding ratio in the initial epoch of model training. However, the proportion rapidly reduces; only approximately 3% of the total samples are excluded in the last epochs. As result, the risk is reduced by excluding many samples in the early training stages, and as the model converges, it learns enough knowledge from almost all samples. Note that, the suggested learning strategy is an effective and easy-to-img -osplleemmlee solution, which significantly improves the mAP of this model by 19.3%.
I INFLUENCE OF CLUSTERING PARAMETER IN USL
Table 12: Training results of CC with the varying parameter of the DBSCAN algorithm.
Method
mAP
R1
R5
R10
Bias
# training clusters
(a)
Without our training strategies
é 0.4
18.3
39,2
49.7
54.8
33.7
2495
c 0.5
21.8
45.8
56.5
61.3
32.0
2090
6 0.6
29.8
57.1
68.5
72.8
32.5
1564
c 0.7
32.0
58.6
71.1
75.9
30.5
1108
é 0.8
8.2
19.1
27.9
32.9
40.7
291
(b) With our training strategies
é 0.4
44.5
74.3
84.0
86.6
24.6
1708
0.5
46.9
75.1
84.6
87.2
24.4
1516
é 0.6
49.1
76.5
85.6
88.3
23.8
1245
c 0.7
46.2
73.5
83.5
86.8
24.0
974
é 0.8
Since the clustering result depends the parameter settings of the clustering algorithm, is possible that the camera bias of the model varies as well. Here, we investigate the influence of the most important parameter of DBSCAN, e, which is the maximum distance between two samples to be neighborhood. The training results of CC on MSMT using several e values are presented in Table 12, where the number of clusters the last training epoch of each model is also shown.
In Table 12()), it is observed that the larger values tend to roughly decrease the camera bias, while the too large value (c 0.8) results the severe performance degradation. larger value can make it easier for samples to cluster together, leading to fewer clusters and larger cluster sizes. Thus, it seems that as the e value increases, the diversity cameras in each cluster benefits from the increased cluster size up to certain point. However, the samples are indiscriminately clustered for too large value, causing poor clustering quality. In this context, setting an appropriate value for e is crucial for model training. We find that 0.7 yields the best result, with the number of clusters (1108) most similar to the number of identities (1041) in the training data. In Table 12(b). it is shown that the performance of the models are considerably improved by training strategies. The result of 0.8 omitted since the number of the generated clusters was extremely low, so the not produced properly with the same data sampler used in the previous experiments.
were
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Published as a conference paper at ICLR 2025
J ADDITIONAL DISCUSSIONS
Limitations The camera-specific feature normalization requires additional computation of mean and variance for each camera, followed by the normalization on the features. The expected running time of these operations increases linearly with the number of data. The calculation of the statistics can be memory- exhaustive process if the number of samples per a camera is large. To solve the problem of computational cost, for example, general under-sampling techniques can be adopted. This issue is left as a topic for future work.
Broader impacts Our work focuses the re-identification technology, which is widely used in real- world applications such as surveillance systems and traffic management solutions. By mitigating bias in target camera domains with simple approach at the inference level, deploying these applications becomes easier, leading to broader use of Al-powered solutions. As a negative societal impact of the work, an improvement the re-identification could be used surveil people in negative manners.
Reproducibility material.
The code for reproducing the experiment results is provided in the supplementary
20


Reviews:
[{"review_id": "rhwHWwDM5b", "reviewer": "Reviewer_UN8K", "summary": "This paper proposes to measure the camera bias and develop a simple normalizaiton methods to address the affects of such bias. Experiments also shows the effectiveness of the proposed method.", "questions": "See weaknesses.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. A thorough investigation of camera bias in reID is a big contribution. This paper provides a quantitative view for bias measuring, which is most welcomed.\n2. The consequently proposed debiasing normalization is very simple yet effective, with sufficient experimental and theorical analysis.\n3. The new findings could advanced exisiting reID methods.", "weaknesses": "1. The adopted reID method is not new, missing some newly proposed methods.\n2. If the number of cameras arise to a big one, e.g., 10k in a region, would the bias disappeared? Or the bias could be supressed by multi-view learning?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "ZOzuC2oRpz", "reviewer": "Reviewer_fCPa", "summary": "This paper empirically investigates camera bias in person re-identification (ReID) models. The authors measure camera bias in ReID models across unseen domains, demonstrating its amplification under data distribution shifts. They analyze the role of feature normalization in mitigating bias, showing it effectively addresses specific bias factors like low-level image attributes and body angles. Furthermore, observing the negative effect of camera-biased pseudo labels on training, the paper proposes simple training strategies to alleviate this bias.", "questions": "See Weaknesses.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "The paper is well-written, with clear motivation and visual aids enhancing the analysis. The main contributions of this work are as follows:\n1. The authors investigate camera bias in ReID models across unseen domain data, offering a comprehensive analysis that spans diverse learning methods and model architectures.\n2. The authors' analysis explains why it is effective for bias mitigation and shows its applicability to detailed bias factors and multiple models.\n3. The authors explore the risk of camera bias inherent in unsupervised learning of ReID models, showing that the performance of existing unsupervised algorithms can be effectively enhanced by simple modifications to reduce the risk.", "weaknesses": "1. The authors’ analysis lacks consideration of the spatial distribution of features, such as a scatterplot of features downscaled to two dimensions using t-SNE. This visualization could reveal feature distributions under camera bias conditions.\n2. The paper primarily addresses domain differences as camera biases. However, another type of camera bias, i.e., the number of samples varies between different cameras, and the identity labels may also be different, is not mentioned in the paper. The authors are welcome to discuss this bias.\n3. The description of the training strategy in 5.3 is too simple. Does discarding bias clustering drastically reduce the training samples? In addition, the parameter settings of the clustering algorithm can affect the clustering results, and whether there is a correlation between the settings of these parameters and the camera bias. This is not discussed by the authors", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "4dVuGbaMkJ", "reviewer": "Reviewer_Grbk", "summary": "This paper discusses camera bias, where it might come from, and a simple normalization solution. In particular, the paper uses a test-time batch to compute the feature mean and variance of each camera and then normalize the features in each camera. The method is shown to be useful across backbones and training methods.", "questions": "None.", "limitations": "", "rating": 8, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "- Overall I like this paper. The authors obviously provided lots of interesting insights. They found that existing methods have camera bias, especially for self-supervised/unsupervised ones. The ones that have dedicated camera-aware design has less camera bias. I believe this observation is new. \n\n- The method is simple and effective. While this method has been used in test-time adaptation, authors use it to address a different problem in person re-id, which should be encouraged. \n\n- Rich insights are provided why camera bias arise. \n\n- I particularly like the fact that this method can be used to deal with human viewpoint bias (Sun and Zheng, 2019). Authors clearly have strong understanding of the field. \n\n- The paper is well written.", "weaknesses": "I don't have particular problem with this paper. If I'm to write this paper, perhaps I will improve the structure a bit more. But given that this paper talks about observations, insights, and an easy solution, perhaps the current struture is fine. \n\nReviewers may have a problem with novelty. But I would champion this way of doing research - there is no need to come up with some new method if the paper is sound and insightful in itself.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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": "fwKgKLcVFe", "reviewer": "Reviewer_ffre", "summary": "To tackle the camera bias issue in person ReID models, this paper offers a comprehensive analysis and proposes a straightforward yet effective test-time post-processing method. Experiments conducted on several ReID datasets demonstrate the method's effectiveness. Overall, the paper is well-written and thorough, but the comparison experiments are somewhat limited, and the novelty of the proposed method may be restricted.", "questions": "1.As similar feature normalization techniques [R1, R2] have been extensively explored in domain generalization (DG) and domain adaptation (DA) tasks, my main concern is that the novelty of this paper may be limited. It would be beneficial to highlight the advantages of this paper compared to existing DG methods for clearer presentation.\n\n2.In my opinion, the effect of the proposed method is similar to that of domain generalization (DG) methods. Therefore, it is not appropriate to directly compare it with supervised or unsupervised methods in the DG setting (training on MSMT and testing on other datasets). For a more thorough comparison, it would be helpful to include additional related DG and DA methods in the experiments. \n\n3.In Table 3, it shows that the improvement achieved by combining the proposed method with the supervised person ReID task (highlighted in red) is limited. More analysis is needed to explain the reasons for this limitation in this context. \n\n4.Since ICE-CAM and PPLR-CAM have demonstrated that unsupervised person ReID methods can benefit from camera information, the improvements observed in the unsupervised ReID setting (highlighted in gray) may be due to the incorporation of camera information in these methods. Could the proposed method further enhance the performance of ICE-CAM?\n\n[R1] Domain-specific batch normalization for unsupervised domain adaptation, CVPR, 2019.\n\n[R2] Batch normalization embeddings for deep domain generalization, Pattern Recognition, 2023.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.The paper is thorough and well-written.\n\n2.The paper offers a comprehensive analysis to address the camera bias issue in person ReID models.", "weaknesses": "1.As similar feature normalization techniques [R1, R2] have been extensively explored in domain generalization (DG) and domain adaptation (DA) tasks, my main concern is that the novelty of this paper may be limited. It would be beneficial to highlight the advantages of this paper compared to existing DG methods for clearer presentation.\n\n2.In my opinion, the effect of the proposed method is similar to that of domain generalization (DG) methods. Therefore, it is not appropriate to directly compare it with supervised or unsupervised methods in the DG setting (training on MSMT and testing on other datasets). For a more thorough comparison, it would be helpful to include additional related DG and DA methods in the experiments. \n\n3.In Table 3, it shows that the improvement achieved by combining the proposed method with the supervised person ReID task (highlighted in red) is limited. More analysis is needed to explain the reasons for this limitation in this context. \n\n4.Since ICE-CAM and PPLR-CAM have demonstrated that unsupervised person ReID methods can benefit from camera information, the improvements observed in the unsupervised ReID setting (highlighted in gray) may be due to the incorporation of camera information in these methods. Could the proposed method further enhance the performance of ICE-CAM?\n\n[R1] Domain-specific batch normalization for unsupervised domain adaptation, CVPR, 2019.\n\n[R2] Batch normalization embeddings for deep domain generalization, Pattern Recognition, 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "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:

Decision: Accept (Spotlight)