Generalizable Person Re-identification Without Demographics
Authors: YiFan Zhang
Feng Li
Zhang Zhang
Baosheng Yu
Liang Wang
Dacheng Tao
Tieniu Tan
Venue: Submitted to ICLR 2023 (2023)
Keywords: Generalizable Person Re-Identification
Distributionally robust optimization
Change-of-measure technique

Abstract: Domain generalizable person re-identification (DG-ReID) aims to learn a ready-to-use domain-agnostic model directly for cross-dataset/domain evaluation, while current methods mainly explore the demographic information such as domain and/or camera labels for domain-invariant representation learning. However, the above-mentioned demographic information is not always accessible in practice due to privacy and security issues. In this paper, we consider the problem of person re-identification in a more general setting, \ie domain generalizable person re-identification without demographics (\textbf{DGWD-ReID}). To address the underlying uncertainty of domain distribution, we introduce distributionally robust optimization (DRO) to learn robust person re-identification models that perform well on all possible data distributions within the uncertainty set without demographics. However, directly applying the popular Kullback-Leibler divergence constrained DRO (or KL-DRO) fails to generalize well under the distribution shifts in real-world scenarios, since the convex condition may not hold for overparameterized neural networks. Inspired by this, we analyze and reformulate the popular KL-DRO by applying the change-of-measure technique, and then propose a simple yet efficient approach, \textbf{Unit-DRO}, which minimizes the loss over a new dataset with hard samples upweighted and other samples downweighted. We perform extensive experiments on both domain generalizable and cross-domain person re-identification tasks, and the empirical results on several large-scale benchmarks show that \iw~achieves superior performance compared to all baselines without using demographics.


Under review as a conference paper at ICLR 2023
GENERALIZABLE PERSON RE-IDENTIFICATION WITH- OUT DEMOGRAPHICS
Anonymous authors Paper under double-blind review
ABSTRACT
Domain generalizable person re-identification (DG-ReID) aims to learn ready- to-use domain-agmonn model directly for cross-datase/domminn evaluation, while current methods mainly explore the demographic information such domain and/or camera labels for domain-invariant representation learning. However, the above-mentioned demographic information is not always accessible in practice due to privacy and security issues. In this paper, we consider the problem person re-identification in more general setting. i.e., domain generalizable person re-identification without demographics (DGWD-ReID) To address the underlying uncertainty of domain distribution, we introduce distributionally robust optimiza- tion (DRO) to learn robust person re-identification models that perform well on all possible data distributions within the uncertainty set without demographics. How- ever, directly applying the popular Kullback-Leibe (or KL-DRO) fails to generalize well under the distribution shifts in real-world scenarios, since the convex condition may not hold for overparameterized neural networks. Inspired by this, we analyze and reformulate the popular KI,-DRO by applying the change-of-measur technique, and then propose a simple yet efficient approach, Unit-DRO, which minimizes the loss over new dataset with hard samples up up-weighted and other samples down-weighted. We perform extensive ex- periments on both domain generalizable and cross-domain person RelD tasks, and the empirical results show that Unit-DRO achieves superior performance compared to all baselines without using demographics.
divergence constrained DRO
INTRODUCTION
Person re- re-identification (ReID) aims to find the correspondences between person images from the same identity across multiple camera views. As illustrated Figure [ previous studies mainly follow three different settings: 1) supervised person ReID Zhang et al. (2020), where training and test data are independently and identically (i.i.d) drawn from the same distribution. Though recent supervised methods have achieved remarkable performance, they are usually non-robust in out-of-distribution (OOD) settings; 2) unsupervised domain adaptative person RelD (UDA-ReID) and cross-domain person ReID (CD-ReID) Luo al. (2020). where UDA-ReID relies large amounts of unlabeled data for retraining and CD-ReID cannot exploit the benefits brought by multi- source domains; 3) domain generalizable person ReID (DG-ReID) Dai et al. (2221)) where the model is trained on multiple large-scale datasets and tested on unseen domains directly without extra data collection/annotaaiio and model updating on new domains. Therefore, DG-ReiD is receiving increasing attention due to its great value in real-world person retrieval applications.
However, current DG-RelD research usually comes at a serious disadvantage: it requires the demo- graphic information (e.g., domain labels CabelsCChoi et al. (202T) Zhao et al. (202T), camera IDs Zhang (22T6)); Dai al. (2021a), and video timestamps Yuan et al. (2020) as the extra supervision for model training. Such demographics implicitly define the variations in training data that the learned model should be invariant or robust to. Unfortunately, the demographic information is usually not available in practice due to the following reasons: 1) the collection of demographics inevitably leads to privacy problems Veale & Binns (2017), e.g., the risks exposing the geographical location and/or the environment information; 2) the collection/annotation of domain labels is very expensive and ethically fraught endeavours Michel et al. (2021); and 3) such coarse- coarse-grained labels and the noise of manual annotation collected domain labels may exacerbate the hidden stratification issue, which hinders a variety of safety-critica applications Creager et al. (202T); Kim & Lee (202T) (see A Appendix A for more discussions). Therefore, shown in Figure ld consider more general

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Under review as a conference paper at ICLR 2023
Supervised Learning
Domsin Gezeralication
Damain Gonoralizatin
Insepersisad Domain Adgetatton
Without Denograpphes (Ourc
Toin
Testo
Toto
K
4
4
P
4
D
D
A
4
4
A
4
A
4
4
4
4
A
(d) Figure 1: An illustration of different person re-identification settings. (a) Supervised person ReID. (c) DG-ReID, (d) DGWD-ReID.
(a)
(h)
(c)
(b) CD-ReID and
setting for person ReID, i.e., Domain Generalizable Person Re-identification Without Demographics (DGWD-RelD), where the model is trained on multiple large-scale scale datasets without demographics.
To address the underlying uncertainty of domain distribution without using demographics, distri- butionally robust optimization (DRO) is a promising paradigm, which explicitly obtains prediction functions robust to distribution shifts Hu et al. (018)) Specifically, DRO considers minimax game: the inner optimization objective is to shift the training distribution within pre-specified uncertainty set so as maximize the expected loss on the test distribution. The outer optimization minimizes the adversarial expected loss. The uncertainty set defined by an f-divergence ball (such as Kullback- Leibler divergence) from the training distribution has been very popular, which is also known as KL-DRO Hu & Hong (2013). However, the convex assumption in KL-DRO usually cannot hold in real-world scenarios, thus leading to inferior performance for overparameterized neural networks.
We address the abooe-mennioned issue and reformulate KL-DRO to first solve the inner step opti- mization problem and then obtain a closed-form expression of the optimal objective. Specifically, different from previous work that converts the minimax DRO problem into single minimization problem by the closed-form expression Hu & Hong (2013), we utilize a change-of-measure technique and reformulate the minimax optimization as an importance sampling problem, termed " By doing this, Unit-DRO avoids bi-level optimization in traditional DRO problems and scales well to overparameterized regimes. Specifically, Unit-DRO upweights samples that are prone to be misclassified and downweights others. It assigns a normalized weight e e//r" /R[ef/r"] of data and label (I, y). where l indicates the error incurred by (Ir, y) and -" is a hyperparameter. During implementation, there are still two main challenges for applying Unit-DRO: 1) it struggles with the hyperparameter parameter T' and we observe that constant T" during training always leads to inferior performance in practice; 2) the normalization factor BBee/r") requires an expectation over the training distribution, which not complementary with the stochastic mini-batch training. To tackle the first problem, we propose a multi-step solution to adaptively determine the value of r" by the training step. We then maintain weight queue to store historical sample weights for a better estimation of E[ee//''] over the training distribution. Compared previous DG-ReID methods, Uni avoids the need for either meta-leaming pipelines or model structure engineering.
each pair
In this paper, we evaluate the proposed Unit-DRO for person ReID by comparing with existing DG- ReID and CD-ReID methods. Unit-DRO outperforms a variety of recent methods with a large margin on both DG-RelID and CD-RelID benchmarks, even including those methods using demographics. To better understand the proposed Unit-DRO, we perform comprehensive ablation studies on several important components, such as the multi-step r' solution and the weight queue. Furthermore, we also visualize the learned weight distributions, t-SNE embeddings, and measure the domain divergence and error set to show the good invariant learning capability of Unit-DRO. Empirical results show that the proposed Unit-DRO can effectively retrieve valuable samples subgroups without demographics.
RELATED WORK
DG-ReID. Generalizable methods are recently proposed learn invariant representations that can generalize to unseen domains [Song et al. (2019); Choi et al. (202)): Zhang et al. (2021b).
In contrast to the word "Group" in Group-DRO Sagawa et al. 2019 where it assigns weights for domains, the word "Unit" in our proposed Unit-DRO assigns weights for samples.
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Under review as a conference paper at ICLR 2023
Existing methods mainly utilize domain divergence minimization strategies or meta-learning pipeline. In view of the current research trend (Table [T. most methods rely on demograph- ics to learn invariant features. Though existing strong baseline Liao & Shao (2020), normal-
ization Jin et al. (020), and augmentation methods Yan et al. (020) require no demographics, they are plug-and-play modules and thus orthog-
onal to the proposed Unit-DRO. Different from existing studies, DGWD-ReID adds strict re- striction on demographies and has ambitious tar- gets that "can we learn invariant features even without demographics? can we partition do- mains better?".
Table 1: The current research trend of DG-RelD.
Method
Source
Domain
Camera
DIR- RelD Zhane et (2021b
Arxiv 21
MeiaBiN Chot el 2021
CVPR 21
M3L 7hab
(021
CVPR
DMG Bai et al 2021
CVPR 21
Fairness without Demographics. Methods in Fairness DDwork et al. (2012) aim to develop a
RaMoE et (0216
CVPR
CBN (BNNZhunng Zhunng
2020
BCCV 20
CAIL all 2020
BCCV 20
model that performs well for worst-case group
QACony Libe
2019
BOCV
Backbone
SNR Jin el aL 2020
CVPR 20
Normalization
assignments according to some fairness criteria for addressing the underperformance in minority subgroups. Though there are several works con- sidering fariness withoul demographics Liu et al. (2021); Creager et al. (021), they mostly evaluate their algorithms in datasets with predefined distribution shifts. Note that DGWD-ReID is more challenging than the category-level recognition problem considered in the existing fariness or w/o demographics study. In DGWD-ReID, the target identities are different from source ones and we need to tackle both domain gap and disjoint label space problems simultaneously. For more discussions about domain generalization, DRO, and cross-domain person RelD (CD-RelD), please also refer to Appendix B
3 METHOD
Problem Formulation. Given the current DG-RelD setting, there is a labeled set of training data from several different domains: P = U Pk and Px = (( v:) ) where N is the number of domains, Ng is the number of images domain Pk. and v; E Xx yi E Y indicate an image and its corresponding label, respectively. During training, we use all aggregated image-label pairs from P. During testing, we evaluate the person retrieval performance on the unseen target domain without any additional model updating. Therefore, the goal of DG-ReID is to learn model fo X + ) that minimizes the empirical error on the unseen target domain G:
min E (a.iee [f(x,, y; BE
(1)
where é is the predefined loss function. This objective encodes the goal of learning model that does not depend on spurious correlations. If model makes decisions according to domain-specific information, it is natural to be brittle in entirely distinct domain. However, previous studies mostly leverage demographics (e.g., domain/camera labels and video timestamps) to clip the spurious correlations, which is not always available in real-world applications. Therefore, consider more general setting where the above-mentioned demographic information is unknown during training, i.e., RG-ReID without demographics or DGWD-ReID, which is in line with the motivation that annotating demographics is expensive and also likely to expose privacy information.
Baseline Algorithm. We introduce the objectives used in our baseline follows. The first one is the cross-entropy loss. Given n training points { 91). ..., (Ix, }.)]. we then have the loss for person identity classification: L.e. = Ex= E(z,, y1; f), where l indicates the cross-entropy loss function. Lmbot-smo is also applied to prevent the model from overfitting to the identity labels. Inspired by recent ReID methods, we further introduce triplet loss to enhance the intra-class compactness and inter-class separability in the embedding space. Following Hermans et al. (2017), given an anchor sample xf. we then evaluate triplet loss using the hardest positive and negative samples, xy and I! within each mini-batch. (or( 2f xx;; () max {d(x; ([(=f.22) æ²;;; 0) d d(2;) x². 2,:00 6) + m, 0}, where .(...) ) indicates a pairwise distance such as the Euclidean distance, and m is the margin between positive and negative pairs. We use BNNeck structure Luo et al. (2019)) to maximize the synergy between Lee and and integrate a mixture of batch normalization and instance normalization with learnable parameters Choi et al. (202T), which are shown very useful for DG-ReID, In the following, we reuse é(zz yr 0) as the sum of both cross-entropy loss and triplet loss.
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3.1 UNIT-DRO
To address the underlying uncertainty of domain distribution without demographics, we introduce Unit-DRO, novel generalization framework that does not require priors about demographics. We first introduce the basic distributionally robust optimization (DRO) framework [Ben-Tal et al. (2009); Rahimian Mehrotra (2019) as follows. In DRO, the worst-case expected risk over a predefined family of distributions Q (termed uncertainty set) is used to replace the expected risk on the unseen target distribution G in Equ Therefore, the objective is as follows,
min max (g( (((x y; 0)]-
BE0 qE
Specifically, the uncertainty set Q encodes the possible test distributions that we want our model to perform well on. If Q contains G. the DRO object can upper bound the expected risk under G.
An important question for using DRO is how to choose the uncertainty set (please also see more discussions in Appendix. Bp. Note that real-world applications, we can obtain only the empirical (training) data distribution. The uncertainty set can thus be constructed by collecting the distributions within a certain distance from the training distribution. For example, previous work may choose KL-divergence Hull & Hong /MM ball [Sinha et al. (2017) around the training distribution, which confers robustness to a wide set of distribution shifts. However, can also lead to overly pessimistic models which optimize for implausible worst-case distributions Duchi et al. (2019). In other words, Q should be sufficiently large to contain G. while it may also contain noisy distribu- tions Michel et al. (202T) Group-DRO Sagawa et al. (2019) thus leverages demographics to define the uncertainty set Q and attains superior OOD performance. Different from Group-DRO, here we consider new extension of DRO to improve OOD generalization without demographics.
KL-DRO. We first introduce the construction of uncertainty set Q based on the KL-divergence ball around the empirical distribution P. Given the KL upper bound (radius) y), we have the uncertainty set Q = {Q: (QI(P) < 7). The min-max problem in Equ can then be reformulated as
min max (( [A(x, y5 0)]-
DEG ()))(Q
Lemma 1 (Modified from Section 2 in Hu & Hong (2013) Assume the model famila 0 E Q and Q to be convex and compact. The loss is continuous and convex for all x E X2 E y. Suppose empirical distribution 'P has density p(r, y)- Then the inner maximum of Equ(3 has a closed-form solution
(x v) = p(x, ((.
Ep [cl(x..r0)//+]]
flz.y:0) log Ep le (l 1)] [)===-))="" where 7" satisfies Ep Eplerte.s10)7-1
n and q" q0 y) is the
T"
optimal density of Q. The min-max problem in Equ is then equivalent to
min T log Ep [x.w:1)=]]] + mT.
(5)
0g0,->0
We refer E as KL-DRO. Unfortunately, the convex condition of KL-DRO is not held for overparameterized neural networks, such that applying it may fail to generalize under the distribution shifts in real-world scenarios. As illustrated in Figure 2 we compare the
30 %
Avemgo mAP
Models KL DRD
D&O
training statisties with the baseline, where KL-DDRO is highly unstable and attains inferior results. Therefore, instead of following KL-DRO to directly use the inner maximum, we reformulate Equ 3 as follows.
Baeline
100
Berations
Figure 2: Training statistics.
min max Re (x(,,; 0) = min max PEB
E(x, y. (1 v)d
sEe Q:KL(QI(P)n)
f(x, ys #) 4(s,, u)
min max 9ee (1I)))
p(z, y) p(z, y) y)dad,
9(H,8) v) E(x, y: 0] p(a, v)
min max
sg8 (
min E U
Eple(te.::)/-1 f(z, y;
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Specifically, to obtain the third line, we apply the change-of-measure meaasue technique. The fourth line replaces the inner maximum by its closed-form solution q" (z, y) in Equ[4 Note that both the value of r" and the normalizer Ep (=(((==))))/=") depend on the expectation of losses over all training data, which is untrackable at each mini-batch based optimization step. For simplicity, as a hyperparameter and take the average over each mini-batch as a preliminary estimator of the normalizer. Therefore, we have the formulation of vanilla Unit-DRO as follows:
can serve r"
((x.):0)/T" 1=1 I (eleeen ((z,, (l..v))))
DRO = min BEE N
where NN is the batch size. However, vanilla Unit-DRO does not work well in practice, and address the following two problems to form a robust Unit-DRO solution.
Multi-Step -². The first problem is that constant hyperparameter T' is usually suboptimal for the whole learning process. As shown in Figure 3 we visualize the densities of the weight (xx.):0)/T (Eppec(c../0)/=)) at different optimization steps when using constant r" (please refer to Section 4.3 for the detailed setups). Specifically, we find that: 1) a small 7" leads to the high variance on the weight distribution and is also sensitive to outliers: 2) a large 7* is so conservative that the weights for all samples are almost similar, and the performance is thus similar to the baseline method. To tackle this problem, we propose a multi-step solution for the hyparameter r", which declines with the training/optimizatioon steps. The intuition behind the multi-step r" is that: at the beginning, use a large r", and the model thus assigns almost similar weights to all samples and cannot identify which sample is more important or not. With the increase of training steps, we decrease the value of T" and improve the weights for important (i.e., hard-to-ding -oittt--iist) samples.
1.00
6:75
030
3.5
2
625
810
6.5
3.0
10
Figure 3: Visualizing the distribution of sample weight at 1k, 5k; 10k, 20k steps, respectively (from left to right). The horizontal axis represents the weight.
Weight Queue M. The second problem is that the expectation over each mini-batch may not be good estimator of the normalizer Ep je((=.w00//r]] To address this problem, we introduce a queue M (w, := =( M==1 to maintain the historical weights, where M depends on the batch size N and determines how well M can estimate Ep (p ((x.)))))) (Please see more empirical analysis in Section 4.3).
Lastly, we have the objective function of Unit-DRO as follows:
NN Σ 1=1
((2.::0)/+-(())
Luiit (0, (t)) min #EE
y: ((x,y:)) v))
l(a,,
(8)
N
( Em.em (w:)
where is the index of training step and T" is a piecewise function of As shown in Figure 2 the training statistics of Unit-DRO is more stable than KL-DRO, and its performance also outperforms baseline methods by a large margin. We depict the online optimization algorithm in Appendix Algorithm [] Note that in Algorithm 1 of Group-DRO [Sagwwa et al. (2019), all samples in the same domain share the same weight, which can be seen as special case of the proposed Unit-DRO. Compared with Group-DRO, one of the key improvements is the implementation trick in that the group weights are updated using exponential gradient ascent instead of picking the group with the worst average loss at each step. Specifically, Group-DRC shows that such improvement is useful for training stability and model convergence but cannot explain why it works. In contrast, the adaptive weights used in this paper are interpretable: the optimal distribution of DRO with KL constraint is proportional to the empirical distribution composite with the exponential term e (x///"
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4 EXPERIMENTS
In this section, we evaluate the proposed Unit-DRO and try to answer the following questions: "without demographies, how does Unit-DRO perform compared to other CD-RelD and DG-ReID methods? what is the influence of different hyperparameters in Unit-DRO? why Unit-DRO improves the baseline?"' To answer the first question, we compare Unit-DRO with baseline methods on both DG-ReID and CD-ReID benchmarks. We then perform detailed ablation studies to answer the second question. Comprchensive analyses are conducted for the third question, e.g., error set analysis, feature visualization, and domain divergence measure.
4.1 EXPERIMENTAL SETUP
Datasets. Following Song et al. 2019); Jia et al. (2019); : Zhang et al. (2021b), we evaluate the
Unit-DRO with multiple data sources (MS), where source domains cover five large-scale ReID datasets, including CUHK02 Li & Wang (203)) CUHK03 Li et al. (2014), Market1501 Zhhng ct al. (2015), DukeMTMC-
Zheng et al. (2017), and CUHK-SY SYSU PersonSearch Xiao et al. (2016). The unseen test domains are VIPeR Gray et al. (2007), PRID Hirzer et al. (201 QMUI. GRID Liu et al. (2012), and i-LIDS Wei-Shi et al. (2009). We include the detailed illustration of datasets and evaluation protocols in Appendix D.I In the CD domain setting, employ M50
and DukcMTMC- MMIDD We alternately construct the two datasets into source and target domains.
Baselines We compare our model with 1) DG-
ReID methods, including AugMining Tamura & Table 2: Summary of different DG-ReID protocols.
(20)g) Murakami (2019), DIMN Song al. (2019), Du-
150m, C2: Cuhk02, C3: Cuhk03, D: DukeMTMC, MT: MSMT17, CS: CUHK-SYSU,
alNorm Jia et al. (2019), SNR Jin et al. (2020).
DDAN Chen et al. (202I). DIR-ReID Zhang
V: ViPeR, P: PRID, G: GRID, I: i-LIDS).
et al. (2021b), and MetaBIN Choi et al.
(002)):
and 2) CD-ReID methods, including Cross-
Protocol
Source
Target
Augmentaation
Grad Shannaar et al.) (2018). QAConv Liao
Shao (2019), -2A-OT Zhou et al. (2020).
(I)
M/D
D/M+V+P+G+I
Color-Jittering
MS+D+M (train)
None
OSN Zhou et al. (2021). SNR Jin et al.
M+D-MT
C3
Color Jittering
(2020). ReRD Zhang et al. (2021b), and
McD+C3-MT
V+P+G+I
Color-Jittering
IN Choi et al. (2021).
Implementation Details. Following previous DG-ReID methods, we use MobileNetV2 Sandler et al. (0018) with the width multiplier of 1.4 as the backbone network, which is initialized using the weights pre-trained on ImageNet Deng et al. (2099). All training images are resized to 256 x 128 pixels and the batch size is N 80. We use the SGD optimizer with momentum 0.9 and the weight decay 5e- - 4. The learning rate starts from 0.01 and then decays to its 0.1 x at 40 and 70 epochs. We also use warmup learning rate schedule at the first 10 epochs. We initialize the multi-step -" with 7* = 100, which is then decayed to 20 and 5 at 40 and 70 epochs, respectively. The default size of the weight queue is M 800. The training process includes 100 epochs. During training, we also use the lmbol-smoothing with the parameter 0.1 and the margin of triplet loss is 0.3. We conduct all the experiments on a machine with 17-8700K CPU, 32G RAM, and four GeForce RTX2080Ti (12GB) GPU cards.
4.2 RESULTS
DG-ReID. Considering that most existing methods can not work without demographic information, we thus compare the proposed Unit-DRO with methods on typical DG-ReID setting, i.e., all other methods use the demographics except Unit-DRO. As shown in Table 3 the proposed Unit-DRO significantly outperforms Group-DRO, while it also achieves either comparable or better performance when compared with recent state-of-thh-ar DG-ReID methods using demographics. By doing this, we hope that the proposed Unit-DRO can serve as a strong bascline for both DG-ReID and DGWD-Rell We observe that current DG RelD methods all apply a utopian model selection method to report their best result by carefully checking the test performance after each training epoch. So the numbers of training epochs corresponding to the best performance are varying for different test datasets We argue that such model selection method is inadvisable. Under the DG setting, we should restrict access to the test domain data for model selection. Thus, we use the last checkpoint and report its results as the final performance over all test datasets. The results in Table 3 show that, without the utopian model selection method, there is always a certain degree of performance decline for existing DG RelD methods, which further indicates the advantages of the proposed Unit-DRO..

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Table 3: Comparison with recent state-of-the-art DG-ReID methods. | means the results of the last checkpoint are reported.
Methods
Average mAP
VIPeR
PRID
GRID
i-LIDS
R-1
R-1
R-5
R-10 mAP
R-1
R-5
R-10
mAP
R-1
R-5
R-10 mAP
R-1
R-5
R-10
mAP
AugMining
49.8
70.8
34.3
56.2
65.7
67.5
76.3
93.0
95.3
DIMN
47.5
57.9
51.2
70.2
76.0
60.1
39.2
67.0
76.7
52.0
29.3
53.3
65.8
41.1
70.2
89.7
94.5
78.4
DualNorm
57.6
61.8
53.9
62.5
75.3
58.0
60.4
73.6
84.8
64.9
41.4
47.4
64.7
45.7
74.8
82.0
91.5
78.5
DDAN
59.0
63.1
52.3
60.6
71.8
56.4
54.5
62.7
74.9
58.9
50.6
62.1
73.8
55.7
78.5
85.3
92.5
81.5
DDAN w/DuulNNom
60.9
65.1
56.5
65.6
76.3
60.8
62.9
74,2
85.3
67.5
46.2
55.4
68.0
50.9
78.0
85.7
93.2
81.2
DIR-ReID
63.8
71.2
38.5
76.9
67.0
69.7
85.8
91.0
77.1
48.2
57.6
79.0
94,8
97.2
83.4
DIR-ReID'
62.3
70.8
57.2
74.1
80.2
64.9
67.6
87.1
91.6
76.6
47.2
66.1
75.4
57.0
77.3
93.3
97.2
84.5
MetaBIN
64.7
72.3
56.9
76.7
82.0
66.9
72.5
88.2
91.3
79.8
49.7
67.5
76.8
58.1
79.7
93.3
97.0
85.5
MetaBINI
71.9
59.3
76.8
67.6
70.6
86.5
91.5
78.2
66.0
56.4
79.5
93.0
97.5
85.5
Group-RRO
57.1
65.9
48.5
68.4
77.2
57.8
66.1
86.5
90.6
74.8
38.7
58.8
66.6
48.6
74.8
90.8
96.8
81.9
Group DRO!
56.7
65.6
48.5
68.9
76.6
58.1
65.4
85.4
89.8
74,1
38.4
58.6
66.1
48.4
74.5
91.0
96.0
81.7
Unit-DRO
72.8
78.2
68.4
73.5
85.3 91.7
79.4
69.3
94.0
97.0
86.2
Table 5: Results for general DG tasks.
PACS P
VL.CS S
Domain
A
C
Avg
C
V
Avg
IRM
79.3
9.4
75.9
97.6
69.7
76.6
77.2
Group-DRO
88.2 0.7
8.
9.2
80
87.2
9.0
6..4
1.2
76.8
77.4
MIXUP
87.4 1.0
80.7
1.0
9..9
79.7
86.4
9.3
66.7
73.3
76.3
78.7
DANN
1.4
80.6 1.0
97.
1.3
85.5
61.3+ 1.
7 1.0
79.7 0.9
77.7
Unit-DRO
88.3 0.1
84.8
0.1
0.1
82.1
87.9
9.11
68.0 0.0
71.
78.8 0.0
79.1
Other DG-ReID Protocols. In addition the most popular evaluation protocols used in Guira- jani & Lopez-Paz (2020) and Tab. [2 we also compare Unit-DRO with other methods using the
following protocols: (1) one-to- to-tllppppppe setting Jin et al. (020); ; (2) multiple-to-one setting Dai ct al.
2021b); (3) multiple-to-one setting Zhao et al. (2021 1 and (4) multiple-to-multipl setting Jin
al. (2020). We summarize the difference between different protocols in Tab. [ As shown in Tab. DRO outperforms other methods with a clear margin in both average mAp and Rank-
1 accuracy. Due the page limit, please also see the results of protocols (2) in Appendix, which demonstrate the robustness of the proposed Unit-DRO across different evaluation protocols. Besides, due to privacy issues, the Duke dataset not appropriate for use. We thus conduct ex- periments under different evaluation protocols but remove the Duke from source/target domains, and Table Sablee@shon shows that the performance margin between Unit-DRO and other baselines becomes larger. Because these protocols are used in different DG-ReID papers, we choose the SOTA method under every protocol for comparison. The privacy issue is also the motivation of our DGWD setting, where images are randomly sampled and are harder to identify which domain it is from.
General Domain Generalization. Apart from Table 4: Comparison with SOTA DG-ReID meth- person ReID, we also compare Unit-DRO with ods under different evaluation protocols, where the other general domain generalization methods, Duke is removed from source and target domains. including IRM Arjovsky et al. (2020), Group- The best accuracy is highlighted by bold.
DRO Sagawa al. (2019), DANN INnnnnn et al. (2016), and Mixup Yan et al.] (2020). For a
Protocol (i)
mAP
Rank-1
Protocol (ii)
mAP
Rank-1
fair comparison, we use test-domain validation, which is one of the most important methods in Gulrajani Lopez-Paz (2020). Specifically, this strategy is an oracle selection since we choose the model maximizing the accuracy on a validation set that follows the distribution of the
SNR
54.3
48.48
RaMoE
31.2
32.4
Ours
58.84
52.7
Ours
41.7
40.4
Protocol (iii)
mAP
Rank-1
Protocol (iv)
mAP
Rank-1
M3L
26.7
27.9
RaMoE
68.8
58.9
Ours
27.8
29.1
Ours
73.2
65.4
test domain. As shown in Table. 5] Unit-DRO consistently outperforms all baseline methods for general domain generalization tasks with a clear margin without using demographics.
4.3 ANALYSIS
Ablation studies. Choosing the best MM [M] denotes the size of memory. |M| 0 means that use the batch data the normalizer Ep (=m))))) which cannot attain the best performance (line 3,5 the table). A huge M contains much out-of-date data and also attains inferior results (line 7). In our experiments, propose to use [ bsz, where bsz is the batch size during training.
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Si 860
-5 (AA -800
114- 300
1080
177
+8000
200 660
3
2
Figure 4: Visualizing the distribution of the sample weight at 1h, 5k: 10k, 20k steps, respectively (from left to right). The horizontal axis is the value of the weight and the vertical axis is the density.
Choosing the best r" Actually, the selection of multi-step r" is not complicated. At the first few epochs, we simply set T to a large value such as 100. In epochs 40, and 70, we decay it to smaller values. When [M| 800, the performance gap will not be much sensitive to different choices of r" Sensitivity. As shown in line 3, even with constant r" and without memory bank, the performance of UnIt-DRO beats both ERM and KL-DDRO by large margin, which is not too sensitive. To avoid grid search for hyper- apeer-parmmeers, we further propose an alternative approach (Linear-decay r"), which models 7 as a decreasing function of training steps. Specifically, r 100(1 - 7 such a method attains 65.0 R-1 aceuracy and 72.3 mAP, which is comparable to the grid search result and simpler.
Sample Weights. Considering that the proposed Unit-DRO Table 6: Ablation studies on different Unit-DRO components.
will upweight and downweight different samples, we thus
visualize the distribution of sample weight to better un- derstand the influences of different components. Specifi- cally, during training, we save the mean and variance of sample weights for every 1k iterations/steps. We assume these weights follow the Gaussian distribution N (j,,, 5) and plot diagrams based on the mean / and variance 5. The æ--oordinate of these diagrams is just the value between [p- [m-3* 66, +=+3 not the real values of weights. Based on the loss values of each sample, we calculate the weights un- der the following two settings: 1) sample weights without the weight queue. In this case, these weights are normal-
Line
LM
R-1
mAP
KL-DRO
KL-DRO
35.7
41.2
ERM
ERM
57.8
66.2
20
0
63.6
71.5
[50.5.3]
800
64.2
72.1
5
[100, 5.
0
63.4
71.6
[100, 5,
800
65
72.2
[100, 5,
4000
63.9
71.9
[100,20,3]
1600
64.2
72
ized in their batches, so the mean all distributions here is 1. As shown in Figure 3 we have already discussed this setting in the former sections; 2) sample weights with different length of weight queue [M] In Figure(4, we show the distribution of sample weight at 1k, 5k, 10k, 20k training steps to indicate how the weight distribution changes during training. Intuitively, we need large [.M] to better estimate Ep Ep(ee(r.vv0)/r" However, as [M| becomes larger, the estimation becomes inaccurate. For example, we consider an extreme case: [M| = T 1, then the queue absolutely contains all training data. Therefore, it is catastrophic to estimate Ep Ep(c (==(x==)))/=) in step T by such a queue. The large queue contains too many old weights which are unsuitable for the current model. Figure depicts the phenomenon, where the distribution with a larger [M] has smaller See Appendix E.2 for more visualization results and discussions about the distribution diagrams of the multi-step
Visualization using t-SNE. We compare the proposed Unit-DRO with MetaBIN and DualNorm through t-SNE visualization. We observe distinct division of different domains in Figure [a which indicates that a speain-specific feature space is learned by the DualNorm. MetaBIN and the proposed Unit-DRO tackle this problem well and the overlaps in Figure 5b and Figure 5e between different domains are more prominent. With (-SNE visualization, we see that Unit-DRO can learn domain-invariant representations while keeping discriminative capability for RelD tasks. However, MetaBIN follows meta- meta-learning pipeline and requires extra expensive demographics. In contrast, the proposed framework Unit-DROn much simpler without using any demographics. We also provide more visualization results and analysis of the discriminative capability in Section E.3 of Appendix.
Domain Divergence. We explore MMD distance Tolstikhin et al. (2016) or A-distance Long et al. (2015) as the measure of domain discrepancy Bevid et al. (2010). As shown in Table 7| we find that Unit-DRO can learn comparable or even more invariant representations compared to MetaBIN, which outperforms Nurm by large margin. We also study the correlation between the weights
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(a) DualNorm
(b) MetaBIN
(c) Unit-DRO
Figure 5:
Visualization of the embeddings on training and test datasets. Query and gallery samples of these unseen datasets are shown using different types of mark. Best viewed in color.
Table 7: Divergence measurement unseen datasets (U), training datasets (T), and all datasets (A).
Method
MMDJ (U)
MMDJ (T)
MMDJ (A)
(U)
A (T)
A J (A)
DualNorm
0.52
0.21
0.41
1.96
1.91
1.88
MetaBIN 0.41
0.19
0.36
1.96
Unit-DRO 0.41
0.19
0.35
1.95
1.89
1.85
for each dataset and the MMD distance. For each dataset, calculate the sum of MMD distance between it to all other datasets. Besides, we calculate the average weights of the final model for shows that for tough dataset (e.g.. CUHK02) that has large divergence other datasets, Unit-DRO assigns a relatively higher average weight. This phenomenon depicts that even without demographics, Unit-DRO can also find meaningful subgroups and upweight them. We can also see that Unit-DRO upweights samples in CUHK-ST SU which has relatively small MMD distance with other datasets. is because the generalization ability divergence, but also some other factors. We discuss these influence factors and perform error set analysis in Section of Appendix. We also plot the MMD distance for every pair of datasets and give further analysis in Section E.5 of Appendix.
each dataset. Table showss
only dependent
domain
5 CONCLUSION AND FUTURE WORK
Traditional DG-ReID methods fail to work in the cases where domain information, such camera labels, or other demographics, are not available due to security and privacy issues. To this end, we introduce DGWD--RelD)
general setting that requires the model learn domain-invariant representations without demographics. To address this problem, we propose Unit-DRO, which is simple yet effective algorithm that substantially improves the model generalization performance with- out requiring expensive demographies during training. Extensive experimental results demonstrate that the proposed Unit-DRO not only achieves comparable better performance compared with other DG-ReID methods using the demographic but also attains superior generalization capability on general domain generalization applications.
Different from the typical classification datasets, where domains are usually partitioned by image styles, RelD datasets have more fine-gra -re-gga variation factors, e.g., the styles of images, camera perspective changes within one dataset, and the shooting conditions at different times on the same camera. We believe that simply specifying each dataset as a domain suboptimal and better domain inference methods that consider the above variation factors will be the subject of future study.
Table 8: Average weight and one-to-all MMD distance for training datasets.
Cuhk02
Cuhk03
Market
Duke
SYSU
Weight MMD
1.02
0.99
0.99
1.00
1.01
1.66
1.17
1.15
1.16
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Generalizable Person Re-identification Without Demographies
AAppeniix
A SUPPLEMENTARY DISADVANTAGES OF DG REID SETTING
1. Privacy Risks on using demographics in ReID tasks. RelD is a research about person which is nat- urally with higher requirements for privacy. Utilizing demographic information will bring the following two concerns. 1) Although the demographic data used in current ReID research is simple, e.g., the different cam- puses in Market 501 and CUHK, the demographic in- formation in practical ReID deployments is much richer and can be obtained casily based on some inherent phys- ical properties of cameras (such as network cameras' MAC address and geographical locations). Thus, the utilization of finer and richer physical properties of cam- eras will increase the leakage risk of the privacy information of pedestrians. 2) The social relationships between different people also possibly to be exposed by demographic information, which may be more sensitive than geographical information. For example, if two-person IDs's images are marked with the same camera, their social relationships may be easily measured by counting their occurrence frequency in all cameras. In summary, the uses of demographic data in real-world RelD applications have evident risks for personal privacy. Thus, we propose the setting of DGWD- ReID, where only a large-scale gallery of pedestrian images without any demographic data, e.g., camera IDs, can be used for training ReID models. Based on the setting of DGWD- ReID, researchers will be forced to exploit invariant features from the training data itself, rather than resort to the side information, i.e., demographic data, so as to reduce the risks of privacy leakage in ReID applications. We also agree that the annotation of subject ID is much more expensive and has more risks personal privacy. We will tackle the challenging problem of unsupervised DG-RelI) in future work. Thanks for your valuable suggestion again!
ViPeR
Market-1501
DukeMTMC
Different Cams
Figure 6: Samples on ReID datasets.
2. The importance of DGWD setting in ML. Learning generalizable models without domain labels becoming an important matter of concern in the ML community Creager et al. (2021); |): Kim & Lee (202)). Our algorithm can also be applied to generalized DG tasks where environment labels are unavailable at training time, either because they are difficult obtain or due privacy.
3. Finally, a previous study Srivastava et al. (2020) shows that how to optimally partition domains in training images that can benefit generalization capability most still unclear, which indicates the direct use of demographic data as domain labels may be inferior for learning representations. Thus, it is also a promising topic for DGWD-RelD as we discussed in the conclusion. In the fairness community, existing work has found that designing rational methodology can find domains that are maximally informative for downstream invariance learners. These domain IDs, and camera IDs make sense for humans, however, the similarities between different domains/camera views vary greatly. How to find more optimal domain partitions for ReID tasks still an open problem.
B EXTENDED RELATED WORK
Domain generalization. Domain/Out-of-diisnii -iiit---iisttiitio generalization Muandet et al. (003)) Zhang ct al. 220 la) aims to learn a model that can extrapolate well in unseen environments. Representative methods like Invariant Risk Minimization (IRM) Arjovsky et al. (2020) and its variant Ahuja et al. (2000) are recently proposed to tackle this challenge. IRM centers on the objective of extracting data representations that lead to invariant prediction across environments under a multi-environment setting. The main difference here is that we propose to learn invariant representations without demographics.
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Tra
Buckbone
Classifler
A
4
Finul
different domains
Component
Ro-Weighting
(x.y,.)
(oppl1iit ((x.y.
Figure 7: An illustration of DGWD-RelDthat propose to reweight training instances without demo- graphic information.
Unsupervised-domen adaptation Person ReID. Unsupervised Domain Adaptation (UDA) tech- nologies have great progress Peng et al. (2020) and have been widely adopted for cross-domain person ReID. The IDA-basce ReID methods usually attempt to transfer the knowledge learned from the labeled source domains to target domains, depending target- images Luo al. (2020);
Huang et al. (2020), features Wang et al. (2018) or metrics Peng et al. (2016). Another group of UDA-based methods Ge et al. (2020): Zhai et al. (2020) propose explore hard soft pseudo labels in the unlabeled target domain using its data distribution geometry. Though UDA-based methods improve the performance of cross-domati ReID certain extent, most of them require large amount of unlabeled target data for model retraining.
Distributionally Robust optimization. Distributionally Robust optimization Ben-Tal et al. (2009) solve robust versions of ERM, which replace the expected risk under the training data distribution with the worst expected risk over pre-defined uncertainty set Q (refer to Rahimian & Mehrotral (2019)
for review). Recent studies constitute Q analytically, such as using moment constraint Delage & Ye (2010); ; guyen et al. (2020). f d-vverggence Hu & Hong (2013): Michel al. (2021), Wasser- stein/MMi MeD ball Sinha et al. (2117): Staib & Jegelka (2019) or coarse-graiee -raaindd mixture models Oren jet al. (2019): Duchi et al. (2019). 9). reformulate KL-constraint DRO to an important sampling problem (Unit-DRO) and propose efficient implementation, which scales to large datasets and
overparameterized neural networks.
C OPTIMIZATION ALGORITHM AND FRAMEWORK DIAGRAM
The optimization algorithm shown in Algorithm I and the diagram is shown in Figure
Algorithm 1: Online optimization algorithm for Unit-DRO.
Input:
training data P, batch size N, learning rate n) training iterations T.
Initial: model parameters 8 and weight queue M = {1)
for 1,..., do
(ri, (:) P
// Data sampling
L = ΣN
l1,, (1, 01:0-11) Ji Emjem(er
//alcullae the reweighted loss
M = M [N (ef 11/00''))000000 (0) 1
//Updete the weight queue
0r + SGD (C,00_1,1)
//Update model parameters
end
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D DETAILED DATASET SETTING
D.1 DATASET DETAILS
Details of the training datasets are summarized in Table and the test datasets are summarized in Table TO All the assets (i.e, datasets and the codes for baselines) we use include an MIT license containing copyright notice and this permission notice shall be included in all copies or substantial portions of the software.
D.2 EVALUATION PROTOCOLS
GRID Liu et al. 2012) contains 250 probe images and 250 true match images of the probes in the gallery. Besides, there are a total of 775 additional images that do not belong to any of the probes. We randomly take out 125 probe images. The remaining 125 probe images and 1025(775 + 250) images the gallery are used for testing.
i-LIDS Wei-Shi et al. 2009) has two versions, images and sequences. The former is used in our experiments. It involves 300 different pedestrian pairs observed across two disjoint camera views and 2 in public open space. We randomly select 60 pedestrian pairs, two images per pair are randomly selected as probe image and gallery image respectively.
PRID2011 1 Hirzer al. (2011) has single-shot and multi-shot versions. We use the former in experiments, The single-shot version has two camera views A and B, which capture 385 and 749 pedestrians respectively. Only 200 pedestrians appear in both views. During the evaluation, 100 randomly identities presented in both views are selected, the remaining 100 identities in view A constitute the probe set, and the remaining 649 identities in view B constitute the gallery set.
VIPeR Gray et al. 2007) contains 632 pedestrian image pairs. Each pair contains two images of the same individual seen from different camera views and 2. Each image pair taken from an arbitrary viewpoint under varying illumination conditions. To compare to other methods, we randomly select half of these identities from camera view 1 probe images and their matched images in view as gallery images.
We follow the single-shot setting. The average rank-k k (R-k) accuracy and mean Average Precision (mAP) over 10 random splits are reported based on the evaluation protocol
For the general DG setting, we use datasets
PACS Li et al. (017) includes 9, 991 images with 7 classes E dog, elephant, giraffe, guitar, horse, house, person from 4 domains d E (art, cartoons, photos, sketches].
VLCS Torralba & Efros 12011) is composed of 10,729 images, classes { bird, car, chair, dog, person from domains d [Caltech101, LabelMe, SUN09, VOC2007).
D.3 BASELINES
The baselines in Table. follow the settings in Jin et al. (2020) where
1. A-IN: naive model where replace all the Batch Normalization(BN) layers in Baseline by Instance Normalization((N).
2. IBN: we add IN only the last layers of Conv1 and Conv2 blocks of Baseline respectively.
3. A-SN: a model where we replace all the BN layers in the Baseline by Switchable Normalization adaptive ensemble version of normalization
(SN). SN Luo al. (2019b) can be regarded techniques of IN, BN, and LN (Layer Normalization).
4. IN: four IN layers added after the first four convolutional blocks/stages of Baseline respectively.
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Table 9; Training Datasets Statistics.
Dataset
IDs
Images
CUHK02
1,816
7,264
CUHK03
1,467
14,097
DukeMTMC-Re-Id
1,812
36,411
Market-
1,501
29,419
CUHK-SYSU
11,934
34,547
Table 10: Testing Datasets statistics.
Probe
Gallery
Dataset
Pr. IDs
Pr. Imgs
Ga. IDs
Ga. imgs
PRID
100
100
649
649
GRID
125
125
1025
1,025
VIPeR
316
316
316
316
i-LIDS
60
60
60
60
E EXTEND EXPERIMENTAL RESULTS
E CROSS-DOMAIN REID PERFORMANCE
For cross-domain evaluation, we use the Market1501 dataset and DukeMTMC-ReID the source/target domains iteratively. For example, "Market-Duke" indicates that the labeled source domain is Mar and DukeMTMC-RRID is the unseen target domain. Since the style variation within single dataset is relatively small, previous DG-RelD methods must utilize fine-grained demographics, e,g., camera labels Zhang et al. (202Ib), or carefully tune all hyperparameters Choi al. (202I). Similar to DG-ReID setting, here Unit-DRC does not any demographic information. As shown in Table we see that the proposed Unit-DRO even achieves a slightly better performance than recent state-of-the-att CD-ReID methods using demographics, suggesting the good cross-domain generalizability of Unit-DRO.
E.2 DISTRIBUTION DIAGRAMS OF STEP 7*
Compared to constant weights with step always have low 8 and are more stable.
E.3 ADDITIONAL t-SNE VISUALIZATION RESULTS
Figure 9 shows the /-SNE results on four unseen datasets. Figure 10 shows the i-SNE results five training datasets and Figure 12 shows the I-SNE results on the Market-Duke benchmark. All of these results demonstrate common pattern, DualNorm Jia et al. (2019) retains large domain divergences and its embedding vector is far from "domain invariant" MetaBIN Choi et al. (2021) utilizes a complex framework and expensive demographics, which is able to reduce domain divergences. Unit- DRO achieves a comparable or even better result than MetaBIN Choi et al. (0021) in a simpler and cheaper paradigm. Consider discrininative capability. Figure 11 visualizes the probe and gallery samples on four test datasets individually. The utopian discrimination result is that every query-galley pair has the closest intra-identity distance and a relatively large inter-identity distance. Figure Tid and Figure| 11b shows that Uni performs well matching on the i-LIDS and the PRID dataset. However, we observe an interesting phenomenon, termed Ideetiiyentiyy Cluster". Specifically, probes and galleries of different identities came together in some clusters. These clusters are always seen on the VIPeR and the GRID datasets (Figure Ilaland Figure TTb). which reveals why Unit-DRO performs much poorly these two datasets.
E.4 IMPLEMENTATION OF DOMAIN DIVERGENCE MEASUREMENT
In general, MMD distance Tolstikhin et al. 2016) is defined by the idea of representing distances be- tween distributions distances between mean embeddings of features. Following MMFA modelLim
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Table Il: Comparison with recent DG-ReID methods using the protocol (I).
Avg
Target:
Tuke
PaID
TargetGRRI
VareetVVPPR
TargetilIID:
Source
Methods
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
A-IN
45.2
44.1
75.3
89.8
24.1
42.7
33.9
21
35.6
27.2
38.1
29.1
64.2
IBN
39.9
39.1
81.1
92.2
21.5
39.2
19.1
12
27.5
19.2
32.1
23.4
58.3
48.3
550
A-SN
42.2
40.9
83.2
93.9
20.1
38
35.4
25
29
22
32.2
23.4
53.4
43.3
IN
45.7
45.1
79.5
90.9
25.1
44.9
35
25
35.7
27.8
35.1
27.5
64
54.2
SNR
50.9
49.6
84.7
94.4
33.6
$5.1
42.2
30
36.7
29
42.3
32.3
65.6
$6.7
Ours
54.7
53.2
83.5
92.2
33.8
55.5
56.7
44.5
40
31
44.7
35.3
69.3
60.7
A-IN
41.2
43.6
21.8
56
64.5
78.9
38.6
29
19.6
13.6
35.1
27.2
67.4
$6.7
26.5
27.4
19
Duke-MIMC
A-SN
42.3
45.5
24.6
35
73
85,9
41.4
32
18.8
12.8
31.3
24.1
64.8
63.3
IN
27.2
SNR
51.3
52.2
33.9
66.7
72.9
84.4
45.4
38
35.3
26
41.2
32.6
79.3
68.7
55.6
36.4
53.23
Table 12: Comparison with recent state-of-the-art CD-ReID methods.
Market-Duke
Duke-Marker
Method
R-1
R-5
R-10
mAP
R-1
R-5
R-10
mAP
CrossGrad
48.5
63.5
69.5
27.1
56.7
73.5
79.5
26.3
QAConv
48.8
28.7
58.6
27.6
L2A-OT
50.1
64.5
70.1
29.2
63.8
80.2
84.6
30.2
OSNet-AIN
52.4
66.1
71.2
30.5
61.0
77.0
82.5
30.6
SNR
55.1
33.6
66.7
33.9
DIR-ReID
54.5
66.8
72.5
33.0
68.2
80.7
86.0
35.2
MetaBIN
55.2
69.0
74.4
33.1
69.2
83.1
87.8
35.9
Unit-DRO
55.5
70.3
74.9
33.8
69.2
83.7
88.0
36.4
ct al. (018), we use the RBF characteristic kernel with bandwidth 02 = 1: 10 to compute the MMD distance. A-distance|Long et al. (0015) can be approximated as (d,, dj) = 22(1-2r7 ((1)) where a is the error of a two-sample classiher distunguishing features of samples from two distinct domains d, dj. Note that have not only two domains. To measure the A-distance or four unseen datasets, we calculate the average mean distance of each domain pair, namely
A(U)
Σ
A(d dz).
6
iml jmi+1
E.5 ADDITIONAL DOMAIN DIVERGENCE MEASUREMENT RESULTS
The MMD distance between every dataset pair of all the datasets plotted in Figure 13a The MMD distance between every dataset pair of five training datasets is shown in Figure 13b and that of four test datasets shown Figure T3 For the training dataset, we find that the CUHK02 dataset remains large divergences with almost all the other domains. Namely, the CUHK02 dataset is more likely to be an duitr--disti dataset and more important to generalization capability. Hence, Unit-DRO assigns relatively higher weights for samples in the CUHK02 dataset. In terms of test datasets, the GRID dataset maintains the largest MMD distance among these datasets. also the reason why Unit-DRO performs badly on the GRID dataset. However, domain divergence is not the only factor that affects generalization performance. Figure 13c shows that the PRID dataset has larger domain divergence than VIPeR. However, on the VIPeR dataset. We exploit the underlying reasons in Section E.6
performs better the PRID dataset than
E.6 ERROR SET ANALYSIS
We select some successfully retrieved pairs? and failure cases in Figure 14 We plot query images and corresponding gallery images at the top and bottom of these figures respectively. Figure 14a
We name query-galler, images pair "successfully retrieved pair" such that the distance between the query image and its corresponding gallery image is the closest in all of the gallery images. Other pairs are named failure cases.
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Table 13: Comparisons against state-of-the-art DG methods for person ReID on evaluation protocol (ii) and (iii). Protocol (ii) and (iii) are both multiple-to-on setting which used in RaMoE Dai et al. (2021b) and Zhao al. 2021 respectively. Unit DRO beats them in both these two settings.
Protocol (ii)
Protocol (iii)
Method
mAP
Rank-1
Rank-5
Rank- 10
Method M3L
mAP
Rank-1 1
RaMoE
35.5
36.6
54.3
64.6
29.9
30.7
Ours 43.8
43.6
65.3
74.5
Ours
30.9
31.1
Table 14; Comparisons against state-of-the-art DG methods for person RelD on evaluation protocol
(iv). Unit DRO outperforms RaMoE Dai al. (2021b) in protocols (iv) by large margin.
Avg
TaID
Target:GRID
Target: VIPeR
Target:iL
Method
mAP
Rank-1 1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
mAP
Rank-1
SNR
64.6
55.4
60.0
49.0
41.3
30.4
65.0
55.1
91.9
87.0
RaMoE
71.3
63.0
66.8
56.9
53.9
43.4
72.2
63.4
92.3
88.4
Ours
76.1
68.0
79.4
71.3
59.8
50.2
77.1
68.9
88.2
81.7
shows that query and gallery images the failure case have a relatively large view change (front and back shooting). In contrast, successfully retrieved query-gallery pairs Figure[44b]inn almost the same camera view. This result shows that Unit-DROcannot well overcome the challenges brought by changes in the camera view. Namely, we can leverage advanced structure in supervised ReID methods to eliminate the sensitivity of Unit-DRO to camera perspective. Figure 14c and Figure [4b show that the camera perspective changes between query and gallery set in the PRID dataset are small. which is one of the reasons why Unit-DRO performs much better on the PRID dataset than the GRID dataser According to error set analysis, we can explain the phenomenon mentioned Section E.5 that Unit-DRO performs superior
[14b]hav
the dataset with relatively high domain divergence (the PRID dataset) than the dataset with low domain divergence (the VIPeR dataset). Figure 14e shows that query-gallery pairs in the VIPeR dataset always maintain camera view changes that more than 90% which is harder to identify compared to the PRID dataset. Finally, the i-LIDS dataset has the lowest MMD distances among other datasets and the camera perspective changes between its query-galless pairs are always small. These good properties enable Unit-DRO to achieve a rank-1 accuracy of 80.7 the i-LIDS dataset. So far, can conclude that all of the domain style divergence, intrinsic characteristics of datasets (camera perspective changes), and model capacity affect the performance of DG ReID and DGWD-ReID methods.
"Another reason is the domain divergence, as we discussed in Scction E.5
larger backhones and advanced leamnnng paradigm always attains better generalization capability.
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70
175
329
71
1220
135
51
23
39
EST
0.30
8.59
3.00
1.2
1.02
3.03
0.05 9 9900-005 L.80 1053.000
9.08
035
1,1
3.02
0.00
0.99
180
3.8
2.02
Figure 8:
Distribution visualization of sample weights (IM) 800 by default) of steps [1000, 50000, 100000, 150000] (from left to right). The horizontal axis represents the weight, and the vertical axis represents the density. [T1 72, T3] means Ti initially and decayed T2 and т3 at 40 and 70 epochs.
Figure 9: The t-SNE visualization of embedding vectors on four unseen target datasets. Query and gallery samples are expressed in different shapes. Best viewed in color.
(a)
(b)
Figure 10: The t-SNE visualization of embedding vectors on five training datasets. Best viewed in color.
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(a) VIPeR
(b) PRID
(c) GRID
(d) i-LIDS
Figure 11: The t-SNE visualization of embedding vectors on four test datasets individually. Best viewed in color.
(b)
Figure 12: The t-SNE visualization of embedding vectors on Market Zheng et al. (2015) and DukeMTMC-Relle Zheng et al. (2017). Models are trained the Market Duke benchmark. Best viewed in color.
22 0.24
0:3
0/06
34
624
6:41
6329
021
6/4
6:44
6:24
623
2.000
633
333
33
6:11
6:33
623
823
(b)
Figure 13: The heatmaps of MMD distance on training and test dataset pairs. (a, b): 0: CUHK02. 1: CUHK03, 2: Market 3: DukeMTMC, 4: CUHK-SYSU, 5: GRID, 6: VIPeR, 7: PRID, 8: i-LIDS. (c): O: GRID, 1: VIPeR, 2: PRID, 3: i-LIDS.
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GRID(F)
GRID(T)
PRID(P)
PRIDCD
(a)
(b)
(c)
(d)
VIPeR(F)
VIPER(T)
ILIDST)
ILIDS(T)
(c)
(f)
(h)
Figure 14: Error set analysis. (a): Failure cases in the GRID datasets. (b) Successfully retrieved pairs in the GRID datasets. (c) Failure cases in the PRID datasets. (d) Successfully retrieved pairs in the PRID datasets. (e): Failure cases in the VIPeR datasets. (f) Successful retrieved pairs in the VIPeR datasets. (g) Failure cases the i-LIDS datasets. (h) Successfully retrieved pairs in the i-LIDS datasets.
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Reviews:
[{"review_id": "qyWZwjOQzU", "reviewer": "Reviewer_42Bp", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "The paper considers domain generalization person re-identification without demographics (DGWD-ReID), which is more difficult than cross-domain person ReID (CD-ReID) and DG-ReID but closer to the real-world scenario because the domain and camera labels are not always available for privacy problem. Innovatively, in order to deal with the uncertainty of domain distribution, the author improved distributionally robust optimization(DRO) by reformulating the minimax optimization as an important sampling problem and named it Unit-DRO. Moreover, the author introduced a multi-step solution and a weighted queue to further improve its performance. The multi-step solution can determine a hyper-parameter adaptively by the training step and the weighted queue can help to estimate the weights better and solve the problem that “stochastic mini-batch training” causes. Sufficient experiments demonstrate that the approach is even better than most approaches with demographics.", "summary_of_the_review": "The approach is innovative for proposing a simple but efficient method for DG-ReID without demographics, and the mathematical derivation, experimental verification, and ablation studies are sufficient and persuasive. These strengths can convince me to accept the paper. However, the paper still needs some improvement in some respects. Firstly, the author should analyze in more detail the adaptability of its approach to the ReID task. Secondly, the author would better present their method more visually with an overall framework. What’s more, the author should analyze the possible reasons for some poor experimental results.", "strength_and_weaknesses": "Strengths:\n(1) Demographics information is not used, and the problem studied is more realistic;\n(2) The author innovatively puts forward the Unit-DRO and it is simple and efficient;\n(3) The author makes an in-depth analysis of the shortcomings of the Unit-DRO and proposes a multi-step solution and a weighted queue to get better performance;\n(4) The experimental results are better than other CD-ReID and DG-ReID methods in the absence of demographics.\n(5) Mathematical analysis and ablation studies are sufficient.\n\nWeaknesses: \n(1) There is a lack of a framework diagram that can visually demonstrate the method proposed;\n(2) Partial experimental results are quite worse than the state-of-the-art, like “Source Market1501 & Target Market 1501”;\n(3) Some of the figures or tables are not clearly illustrated, like the horizontal axis of each chart in Figure4;\n(4) Lack of detailed analysis of the ReID task itself and the adaptability of the proposed method to the ReID task.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "ohitbUN_P_", "reviewer": "Reviewer_2SbQ", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "This paper considers a problem of person re-identification in a more general setting, i.e., domain generalizable person ReID without demographics. To address this, it introduces distributionally robust optimization (DRO) to learn robust person ReID models that perform well on all possible data distributions within the uncertainty set without demographics. Furthermore, it analyzes and reformulates the popular KL-DRO by applying the change-of-measure technique, and then propose a simple yet efficient approach, Unit-DRO. Extensive experiments are conducted on several ReID tasks to show the superiority of the proposed method.", "summary_of_the_review": "This paper proposes a novel Unit-DRO approach to tackle the task of domain generalizable person ReID without demographics. Although the proposed method is novel, and achieves superior performance on both person ReID and general DG tasks. there are some limitations as mentioned in the weakness. So I give the score of 5 and would like to hear the feedback from the author.", "strength_and_weaknesses": "**Strength**\n1. The proposed DGWD-ReID setting is interesting, and the proposed method is novel.\n2. The paper is easy to understand and well written.\n3. Extensive experiments are conducted to demonstrate the superiority of the proposed method.\n\n**Weakness**\n1. In Line 7 of Algo. 1, does the model trained with Unit-DRO only use the cross-entropy loss function? Since in the last paragraph of Sec. 3, both cross-entropy and triplet losses are used as the baseline. \n2.  Ablation studies in Tab. 7 are a little simple. It is unable to show a clear advantage of the proposed component by comparing results from two or three lines. Besides, how to select the optimal values for $\\tau^{*}$ and |M|. I think they are either important or sensitive to the results.  As mentioned in Sec. 4.1, the scheme of multi-step $\\tau$ seems complicated.  And in Tab. 7, it just simply compares the results with two options of M\n3. In Tab. 3, there are two lines reporting the results of the same methods but with different numerical values\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "4: All of the claims and statements are well-supported and correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work."}, "extra_text": {}}, {"review_id": "hGnn5O1rtg", "reviewer": "Reviewer_xvAD", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "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": "", "main_review": "", "summary_of_the_paper": "This paper proposes a more general setting for person re-identification, i.e., domain generalizable person re-identification without demographics (DGWD-ReID). Based on the distributionally robust optimization (DRO) method for addressing the underlying uncertainty of domain distribution, it proposes a variant of DRO, named Unit-DRO, specifically for DGWD-ReID. Experimental results show the better performance of the proposed method compared to other baselines. ", "summary_of_the_review": "This paper proposes the Unit-DRO for DG-ReID without demographic information, which is novel and requires only subject ID labels. However, there are also some concerns and unclear points mentioned above. Therefore, I’m inclined to score “5”.", "strength_and_weaknesses": "Pros: \n(1)\tThe use of DRO for DGWD-ReID is technically sound. \n(2)\tModified from the existing KL-DRO, the proposed Unit-DRO seems to work well for DGWD-ReID.\n(3)\tThe extensive experiments show the effectiveness of the proposed method.\n\nCons:\n(1) This paper claims a more general setting, i.e., DGWD-ReID. However, it's more of an easy-to-use approach, using only subject ID labels in DG-ReID. It can indeed be considered an improvement, but not a setting that is only suitable for real-world scenarios. Only the domain label and camera ID label don't seem to reveal so much location and environment information, which might be easier to know from the image background itself. Also, compared to the domain and camera ID, annotating subject ID seems to be much more expensive. \n\n(2) This paper largely criticizes the demographic information (e.g., domain labels, camera IDs) used in existing DG-ReID methods. Even if the mentioned concerns (e.g., mainly privacy concerns) are true, we can also redefine demographic information without involving privacy concern. For example, we can apply clustering on all training sources, and assign the domain labels to different cluster centers (e.g., natural background or structured background). Training and the test evaluation might benefit if such obtained demographic information is also involved.\n\n(3) As mentioned in the “Baseline Algorithm” of Section 3, similar to MetaBIN, the proposed method uses a mixture of batch normalization and instance normalization with learnable parameters. However, as discussed in MetaBIN, direct Batch-Instance Normalization may not generalize successfully on a given unseen target domain, so they use a meta-learning pipeline. How does the proposed method solve the problem without a meta-learning pipeline? Some discussion is necessary.\n\n(4) As for the ablation study in Table 7, all of them are for different Unit-DRO components. How about the baseline without Unit-DRO and the baseline with KL-DRO (as Unit-DRO is modified from KL-DRO)?\n\n(5) Some methods or labels in Table 5 and 6 are not defined. For example, in Table 5, what are A-IN, IBN, A-SN, IN stand for? In Table 6, what are the domains “P”, “A”, “C”, “S”, “V” and “L”? Besides, in Table 3, there are two MetaBINs. What is the difference?\n\n(6) Minor points: in the abstract, “unweighted” might be “up-weighted”.\n", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"correctness": "3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.", "technical_novelty_and_significance": "3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.", "empirical_novelty_and_significance": "2: The contributions are only marginally significant or novel."}, "extra_text": {}}]

Author rebuttal:

Decision: Reject