Pith. sign in

REVIEW 2 major objections 6 minor 105 references

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching

T0 review · 2 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper presents the first systematic survey of domain-generalizable person re-identification, organizing the field into seven method families and comparing their performance.

desk verdict Useful first survey of DG-ReID with a sensible taxonomy, but the headline performance tables don't implement the stated protocols, so the 'ReNorm leads' conclusion is not supported. read the letter →

arxiv 2506.12413 v1 pith:IZIKJT77 submitted 2025-06-14 cs.CV

Hyeonseo Lee , Juhyun Park , Jihyong Oh , Chanho Eom This is my paper · ORCID
classification cs.CV
keywords personre-identificationdomaingeneralizationdomain-invariantrepresentationlearningimage-basedretrievalnormalization-basedmethodsmeta-learningCLIP-basedevaluationprotocols
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish a reliable map of Domain-Generalizable Person Re-identification (DG-ReID), the setting where a model learns to match people using several labeled source domains and is then tested on an unseen domain with no target data. It claims to be the first full survey dedicated to DG-ReID, grouping methods into seven families and comparing them under three standard evaluation protocols. If the map is correct, researchers gain a shared vocabulary, a baseline leaderboard, and evidence about which designs actually generalize. The survey also argues that current leaders are approaching saturation on small test sets, while harder targets such as MSMT17 still leave room for progress.

What carries the argument

The load-bearing object is the formal DG-ReID setting itself: $K$ source domains $D_S = \{D_1, \ldots, D_K\}$ with disjoint identity label spaces ($Y_i \cap Y_j = \emptyset$), which rules out direct cross-domain identity matching and forces the model to learn features that are both domain-invariant and identity-discriminative. Around this setting the survey builds two analytical instruments: a seven-category, module-centric taxonomy of methods, and a three-protocol evaluation scheme (Protocol-1 tests on four small unseen datasets; Protocol-2 and Protocol-3 leave out one large dataset as target). These instruments do the work of converting a scattered literature into a leaderboard, with Tables 3 and 4 carrying the comparative evidence.

What would settle it

Re-run every method in Tables 3 and 4 from its released code under a single fixed source-domain mix for each protocol and compare the reproduced mAP and Rank-1 values; if the top performers change or the average numbers differ materially from the table, the survey's leaderboard conclusions (ReNorm and BAU lead; performance is approaching saturation) would be overturned.

Watch

Extended reading notes

Core claim

The paper's central claim is that existing DG-ReID research can be organized around a single pipeline—a backbone (usually ResNet-50, MobileNetV2, or ViT) trained on multiple source domains—plus one or more generalization modules, and that those modules fall into seven categories: normalization-based, mixture-of-experts-based, memory-based, meta-learning-based, data-driven, CLIP-based, and others. It backs this taxonomy with a performance comparison under three protocols, finding that normalization-focused ReNorm leads on Protocol-1 and Protocol-3, BAU and ReNorm lead on Protocol-2, and that MSMT17 remains the hardest target. The paper also treats DG-ReID as a heterogeneous domain-generalization problem with disjoint identity label spaces across source domains, and claims that its techniques carry over to the related VI-ReID task.

Load-bearing premise

The load-bearing assumption is that the numbers in the comparison tables were copied correctly from the original papers and that the three evaluation formats are comparable across methods, even though the rows of Table 3 use different training-dataset combinations while the caption presents them as one common protocol.

Editorial extensions

If this is right

  • New DG-ReID papers can position their contribution against a named category (normalization, mixture-of-experts, memory, meta-learning, data-driven, CLIP, or other) and compare with the same standard protocols.
  • Normalization-based designs, especially ReNorm's remix and emulation normalization, currently represent the strongest surveyed approach on the average of Protocol-1 and Protocol-3.
  • The three evaluation protocols give the field a common yardstick, with Protocol-2 the most severe domain shift and Protocol-3 the most realistic deployment scenario.
  • MSMT17 remains a bottleneck: even the best Protocol-3 method reaches only 27.8% mAP there, so claims of saturation need qualification.
  • The VI-ReID case study suggests that DG-ReID modules (normalization, memory, gradient reversal, graph matching) transfer across tasks that share a domain-gap structure.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the seven categories are not mutually exclusive—several leading methods combine normalization with meta-learning or mixture-of-experts structures—so a future survey might reorganize the field by training objective (invariance, diversity, alignment) rather than by module type.
  • Editorial inference: because Protocol-1 mixes different source-domain sets across rows of Table 3, the leaderboard may compare methods trained on different amounts of data; a common-source re-run would be needed to fully trust the rankings.
  • Editorial inference: the attention-map comparison suggests a measurable diagnostic—person-centric attention share—that could be added to DG-ReID evaluation beyond the qualitative example shown.
  • Editorial inference: the low MSMT17 mAP hints that target-domain difficulty, not source diversity alone, sets the ceiling for DG-ReID, which would make harder benchmark design as important as new modules.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper presents a survey of domain-generalizable person re-identification (DG-ReID), a setting where models are trained on multiple source domains and evaluated on unseen target domains without any target-domain data. The authors review the standard architecture components (backbones, multi-source input configurations), propose a taxonomy of DG-ReID methods (normalization-based, mixture-of-experts, memory-based, meta-learning, data-driven, CLIP-based, and others), provide a case study on visible-infrared ReID, and assemble performance comparisons under three evaluation protocols. The paper claims to be the first systematic survey dedicated to DG-ReID and concludes that ReNorm and BAU are state-of-the-art, with performance approaching saturation on some benchmarks.

Significance. If the empirical comparisons are reliable, the survey provides a useful and timely map of a rapidly growing field. The taxonomy is broadly sensible, the attention-map analysis and the VI-ReID case study are valuable additions, and the authors ship a curated resource list on GitHub. However, the central empirical claims rest on performance tables that are not internally consistent: Table 3 does not implement the Protocol-1 defined in Section 5.3, and Section 5.4 contradicts Table 4 regarding M3L. These issues undermine confidence in the headline conclusions about which methods lead the field. The survey's taxonomic and qualitative contributions are significant, but the performance analysis requires major revision.

major comments (2)
  1. [§5.3 and Table 3] Table 3 does not implement Protocol-1 as defined in Section 5.3 and Table 2. The protocol fixes the training set to Full-(M+C2+C3+CS), but individual rows in Table 3 list different source mixes: 'M+D' for SNR, 'C2+C3+CS' for DMG-Net/RaMoE/MDA/DTIN-Net, and 'M+C2+C3+CS' for M3L/META/MetaBIN/ACL/ISR/PAOA/BAU/ReNorm, with DIMN lacking a source entry entirely. Training on three, four, or five source datasets, and with or without the withdrawn DukeMTMC set, constitutes a different task; the claimed 1.0% mAP and 2.7% Rank-1 advantage of ReNorm over BAU in Section 5.4 could therefore be a source-mix artifact rather than evidence of method superiority. Please either restrict Table 3 to rows that actually use the Protocol-1 training set or explicitly state that the comparison is not controlled and adjust the claims accordingly.
  2. [§5.4 and Table 4] The text in Section 5.4 states: 'DIMN, DMG-Net, M3L, RaMoE, DTIN-Net and MDA are excluded from this comparison due to no results reported for Protocol-2 and -3.' However, Table 4 includes M3L rows under both Protocol-2 and Protocol-3. This is a direct internal inconsistency. It indicates that the performance tables were assembled without a consistent provenance audit, which is exactly the condition needed for the survey's headline performance conclusions to be reliable. Please correct the text or the table and re-verify each entry against the cited source.
minor comments (6)
  1. [Reference [28]] Reference [28] misspells 'CLIP-FGDI' as 'CILP-FGDI'; please correct the title.
  2. [Table 1 / Reference [88]] The iLIDS dataset is attributed to reference [88] with venue 'ECCV'18', but that reference is a paper on unsupervised tracklet association, not the original source of the iLIDS dataset, and the venue does not match the original iLIDS publication. Please cite the correct dataset reference and update the venue in Table 1.
  3. [Tables 3 and 4 vs. bibliography] ReNorm is listed as 'ECCV'24' in Tables 3 and 4 but reference [31] says ECCV 2025; BAU appears as both 'NeurIPS'24' and 'NIPS'24' across the tables. Please make venue and year entries consistent between the tables and the bibliography.
  4. [§3, §7, and Figure 2] Section 3 says methods are categorized into 'eight methodological aspects,' then lists seven categories; Figure 2 and Section 7 consistently refer to seven modules. Please align the count.
  5. [§6.3, Figure 8] The text says 'As visualized in Fig. 9,' but the corresponding figure is labeled 'Figure 8: Visualization of frequency decomposition results.' Please correct the cross-reference.
  6. [Table 4 caption] The shared column headers 'M+MS+CS→C3' etc. obscure the difference between Protocol-2 (only training subsets) and Protocol-3 (full source domains); please add explicit phrasing in the caption, such as 'Full-' for Protocol-3, to avoid confusion.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the survey's taxonomy and performance tables are compiled from external literature; the few self-citations are incidental and not load-bearing.

full rationale

This is a survey paper rather than a derivational paper, so the circularity patterns that require a claimed derivation to reduce to its inputs do not apply. The central products are a taxonomy (Fig. 2, Section 3), a cross-task case study (Section 4), and performance comparisons (Tables 3-4). The taxonomy is a categorical organization of methods cited from the external literature; it does not derive or generate those methods' results. The performance rankings, including the statements that ReNorm and BAU lead and that performance is approaching saturation, are transcriptions or reports of numbers published in the cited primary papers; they are therefore not predictions made from the survey's own fitted parameters or definitions. The self-citations in the reference list (refs 4, 8, 11) are used only as examples of general ReID literature in the introduction and do not justify any domain-generalization claim, protocol, or ranking. No uniqueness theorem from the authors' prior work is invoked, and no ansatz is smuggled in via self-citation. A separate correctness concern, not a circularity, is that Table 3 lists different training-source combinations per row (e.g., M+D for SNR, C2+C3+CS for DMG-Net, M+C2+C3+CS for M3L) while Section 5.3 defines Protocol-1 as Full-(M+C2+C3+CS) and the caption says DukeMTMC is excluded; this weakens the comparability of the headline numbers but does not make the survey's claims equivalent to its inputs by construction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

A survey introduces no fitted parameters or invented entities. The load-bearing assumptions are about the completeness of the literature search and the fidelity of the transcribed performance numbers, both of which are limitations stated or implied in the paper.

assumptions (4)
  • domain assumption The keyword-based literature search covering 2018-2025 is complete enough to support the survey's taxonomy.
    Section 1 footnote 1 describes the collection method; no systematic review protocol, inclusion/exclusion criteria, or coverage audit is provided.
  • domain assumption Performance numbers in Tables 3 and 4 are faithfully transcribed from the cited primary papers.
    Section 5.4 reports SOTA comparisons without source code or raw outputs; internal citation inconsistencies, such as the ReNorm venue/year mismatch, raise doubt.
  • domain assumption The evaluation protocols defined in Section 5.3 are applied consistently across compared methods.
    Table 3 mixes methods trained on different source-domain sets such as M+D and M+C2+C3+CS, while the caption claims a common protocol.
  • standard math Standard definitions of CMC, mAP, and triplet loss as given in Eqs. 6, 9, and 10 are taken from prior literature without proof.
    These are standard evaluation definitions used across the field, not the survey's own contribution.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching." pith.science (2026). https://pith.science/paper/IZIKJT77

@misc{pith2026250612413,
  author       = {Pith},
  title        = {Pith review of: Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IZIKJT77}},
  note         = {Machine review of arXiv:2506.12413}
}
read the original abstract

Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and lighting conditions. To address this issue, Domain-Adaptive ReID (DA-ReID) methods have been proposed. These approaches incorporate unlabeled target domain data during training and improve performance by aligning feature distributions between source and target domains. Domain-Generalizable ReID (DG-ReID) tackles a more realistic and challenging setting by aiming to learn domain-invariant features without relying on any target domain data. Recent methods have explored various strategies to enhance generalization across diverse environments, but the field remains relatively underexplored. In this paper, we present a comprehensive survey of DG-ReID. We first review the architectural components of DG-ReID including the overall setting, commonly used backbone networks and multi-source input configurations. Then, we categorize and analyze domain generalization modules that explicitly aim to learn domain-invariant and identity-discriminative representations. To examine the broader applicability of these techniques, we further conduct a case study on a related task that also involves distribution shifts. Finally, we discuss recent trends, open challenges, and promising directions for future research in DG-ReID. To the best of our knowledge, this is the first systematic survey dedicated to DG-ReID.

Figures

Figures reproduced from arXiv: 2506.12413 by the authors.

Figure 1
Figure 1. Train-test settings in Person ReID and the typical framework of DG-ReID. (a) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Taxonomy of Domain Generalizable person re-identification (DG-ReID). [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Visualization of Normalization Effects on Feature Distribution. BN [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Comparison of Independent and Shared Expert Methods. (a) Independent [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]
Figure 5
Figure 5. Figure 5: Overview of data-driven methods for DG-ReID. (a) Unlabeled data learning [PITH_FULL_IMAGE:figures/full_fig_p023_5.png]
Figure 6
Figure 6. Figure 6: Examples of query (left) and gallery (right) images from different datasets [PITH_FULL_IMAGE:figures/full_fig_p033_6.png]
Figure 7
Figure 7. Figure 7: Attention map visualization of embedding vectors on MSMT17 query images. [PITH_FULL_IMAGE:figures/full_fig_p039_7.png]
Figure 7
Figure 7. Figure 7: Both models share a ResNet-50 backbone for fair comparison. The [PITH_FULL_IMAGE:figures/full_fig_p043_7.png]
Figure 8
Figure 8. Figure 8: Visualization of frequency decomposition results using samples from four dif [PITH_FULL_IMAGE:figures/full_fig_p046_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

105 extracted references · 74 canonical work pages

  1. [28]

    H. Zhao, L. Qi, X. Geng, CILP-FGDI: Exploiting vision-language model for generalizable person re-identification, IEEE Transactions on Information Forensics and Security (2025)

    work page 2025

  2. [1]

    Zheng, L

    L. Zheng, L. Shen, L. Tian, S. Wang, J. Wang, Q. Tian, Scalable person re-identification: A benchmark, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015

    2015

  3. [3]

    W. Li, R. Zhao, T. Xiao, X. Wang, Deepreid: Deep filter pairing neural network for person re-identification, in: Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), 2014

    2014

  4. [4]

    H. Yu, J. Park, K. Kang, S. Jeong, Smagnet: Scaled mask attention guided network for vision-based gait analysis in multi-person environ- ments, IEIE Transactions on Smart Processing & Computing (2024)

    2024

  5. [5]

    L. Wei, S. Zhang, W. Gao, Q. Tian, Person transfer gan to bridge do- main gap for person re-identification, in: Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), 2018. 50

    2018

  6. [6]

    Y. Sun, L. Zheng, Y. Yang, Q. Tian, S. Wang, Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline), in: Proceedings of the European Conference on Computer Vision (ECCV), 2018

    2018

  7. [7]

    G. Wang, Y. Yuan, X. Chen, J. Li, X. Zhou, Learning discrimina- tive features with multiple granularities for person re-identification, in: Proceedings of ACM International Conference on Multimedia (ACM MM), 2018

    2018

  8. [8]

    C. Eom, B. Ham, Learning disentangled representation for robust per- son re-identification, in: Advances in Neural Information Processing Systems (NeurIPS), 2019

    2019

Show all 105 references
  1. [9]

    K. Zhu, H. Guo, Z. Liu, M. Tang, J. Wang, Identity-guided human semantic parsing for person re-identification, in: Proceedings of the European Conference on Computer Vision (ECCV), 2020

    2020

  2. [10]

    H. Zhao, X. Li, Y. Wu, X. Li, Spindle net: Person re-identification with human body region guided feature decomposition and fusion, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017

    2017

  3. [11]

    C. Eom, B. Ham, Disentangled representations for short-term and long- term Re-Identification, IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)

    2022

  4. [12]

    Panda, A

    R. Panda, A. Bhuiyan, V. Murino, A. K. Roy-Chowdhury, Unsuper- vised adaptive re-identification in open world dynamic camera net- works, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

    2017

  5. [13]

    G. Chen, Y. Lu, J. Lu, J. Zhou, Deep credible metric learning for un- supervised domain adaptation person re-identification, in: Proceedings of the European Conference on Computer Vision (ECCV), 2020

    2020

  6. [14]

    G. Lee, S. Lee, D. Kim, Y. Shin, Y. Yoon, B. Ham, Camera-driven representation learning for unsupervised domain adaptive person re- identification, in: Proceedings of the IEEE/CVF International Confer- ence on Computer Vision (ICCV), 2023. 51

    2023

  7. [15]

    K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proceedings of the IEEE Conference on Computer Vi- sion and Pattern Recognition (CVPR), 2016

    2016

  8. [16]

    X. Jin, C. Lan, W. Zeng, Z. Chen, Style normalization and restitu- tion for generalizable person re-identification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

    2020

  9. [17]

    S. Choi, T. Kim, M. Jeong, H. Park, C. Kim, Meta batch-instance normalization for generalizable person re-identification, in: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021

    2021

  10. [18]

    Y. Cho, J. Kim, W. J. Kim, J. Jung, S. E. Yoon, Generalizable person re-identificationviabalancingalignmentanduniformity, arXivpreprint arXiv:2411.11471 (2024)

    2024 arXiv

  11. [19]

    Z. Dou, Z. Wang, Y. Li, S. Wang, Identity-seeking self-supervised rep- resentation learning for generalizable person re-identification, in: Pro- ceedings of the IEEE International Conference on Computer Vision (ICCV), 2023

    2023

  12. [21]

    Y. Zhao, Z. Zhong, F. Yang, Z. Luo, Y. Lin, S. Li, N. Sebe, Learning to generalize unseen domains via memory-based multi-source meta- learning for person re-identification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021

    2021

  13. [22]

    B. Jiao, L. Liu, L. Gao, G. Lin, L. Yang, S. Zhang, P. Wang, Y. Zhang, Dynamically transformed instance normalization network for general- izable person re-identification, in: Proceedings of the European Con- ference on Computer Vision (ECCV), 2022

    2022

  14. [23]

    Sandler, A

    M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L. Chen, Mobilenetv2: Inverted residuals and linear bottlenecks, in: Proceedings of the IEEE 52 Conference on Computer Vision and Pattern Recognition (CVPR), 2018

    2018

  15. [24]

    J. Song, Y. Yang, Y. Z. Song, T. Xiang, T. M. Hospedales, General- izable person re-identification by domain-invariant mapping network, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019

    2019

  16. [25]

    H. Ni, Y. Li, L. Gao, H. T. Shen, J. Song, Part-aware trans- former for generalizable person re-identification, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  17. [26]

    Dosovitskiy, L

    A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale, arXiv preprint arXiv:2010.11929 (2020)

    2020 arXiv

  18. [27]

    Radford, J

    A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al., Learning transferable visual models from natural language supervision, in: Proceedings of the International Conference on Machine Learning (ICML), 2021

    2021

  19. [29]

    H. Zhao, L. Qi, X. Geng, CLIP-DFGS: A hard sample mining method for clip in generalizable person re-identification, ACM Transactions on Multimedia Computing, Communications, and Applications (2024)

    2024

  20. [30]

    Zhang, S

    P. Zhang, S. Xiang, Y. Nie, C. Pan, C. Zhang, W. Zhang, Adap- tive cross-domain learning for generalizable person re-identification, in: Proceedings of the European Conference on Computer Vision (ECCV), 2022

    2022

  21. [31]

    R. Nie, J. Ding, X. Zhou, X. Li, Rethinking normalization layers for domain generalizable person re-identification, in: Proceedings of the European Conference on Computer Vision (ECCV), 2025. 53

    2025

  22. [32]

    B. Xu, Y. Zhao, Y. Jin, H. Hu, Z. Zhang, S. He, Mimic embedding via adaptive aggregation: Learning generalizable person re-identification, in: Proceedings of the European Conference on Computer Vision (ECCV), 2022

    2022

  23. [33]

    Q. Li, S. Gong, Mitigate domain shift by primary-auxiliary objec- tives association for generalizing person ReID, in: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024

    2024

  24. [34]

    Y. Dai, Y. Liu, F. Yang, J. Wu, Y. Zhuang, Generalizable person re- identification with relevance-aware mixture of experts, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition (CVPR), 2021

    2021

  25. [35]

    Hermans, L

    A. Hermans, L. Beyer, B. Leibe, In defense of the triplet loss for person re-identification, arXiv preprint arXiv:1703.07737 (2017)

    2017 arXiv

  26. [36]

    Ulyanov, A

    D. Ulyanov, A. Vedaldi, V. S. Lempitsky, Instance normalization: The missing ingredient for fast stylization, arXiv preprint arXiv:1607.08022 (2016)

    2016 arXiv

  27. [37]

    S.Ioffe, Batchnormalization: Acceleratingdeepnetworktrainingbyre- ducing internal covariate shift, arXiv preprint arXiv:1502.03167 (2015)

    2015 arXiv

  28. [38]

    Y. Li, N. Wang, J. Shi, J. Liu, X. Hou, Revisiting batch normaliza- tion for practical domain adaptation, arXiv preprint arXiv:1603.04779 (2016)

    2016 arXiv

  29. [39]

    W. G. Chang, T. You, S. Seo, S. Kwak, B. Han, Domain-specific batch normalization for unsupervised domain adaptation, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019

    2019

  30. [40]

    Huang, S

    X. Huang, S. Belongie, Arbitrary style transfer in real-time with adap- tive instance normalization, in: Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision (ICCV), 2017

    2017

  31. [41]

    H. Nam, H. Lee, J. Park, W. Yoon, D. Yoo, Reducing domain gap via style-agnostic networks, arXiv preprint arXiv:1910.11645 (2019). 54

    2019 arXiv

  32. [42]

    X. Pan, P. Luo, J. Shi, X. Tang, Two at once: Enhancing learning and generalization capacities via ibn-net, in: Proceedings of the European Conference on Computer Vision (ECCV), 2018

    2018

  33. [43]

    Jacobs, M

    R. Jacobs, M. Jordan, S. Nowlan, G. Hinton, Adaptive mixtures of local experts, Neural Computation (1991)

    1991

  34. [44]

    Jordan, R

    M. Jordan, R. Jacobs, Hierarchical mixtures of experts and the em algorithm, Neural Computation (1994)

    1994

  35. [45]

    Zhong, Z

    T. Zhong, Z. Chi, L. Gu, Y. Wang, Y. Yu, J. Tang, Meta-dmoe: Adapt- ing to domain shift by meta-distillation from mixture-of-experts, in: Advances in Neural Information Processing Systems (NeurIPS), 2022

    2022

  36. [46]

    M. Wang, J. Yuan, Z. Wang, Mixture-of-experts learner for single long- tailed domain generalization, in: Proceedings of the 31st ACM Inter- national Conference on Multimedia (ACM MM), 2023

    2023

  37. [47]

    S. Liao, L. Shao, Interpretable and generalizable deep image matching with adaptive convolutions, arXiv preprint arXiv:1904.10424 (2019)

    2019 arXiv

  38. [48]

    Zhong, L

    Z. Zhong, L. Zheng, Z. Luo, S. Li, Y. Yang, Invariance matters: Ex- emplar memory for domain adaptive person re-identification, in: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 598–607

    2019

  39. [49]

    S. Liao, L. Shao, Interpretable and generalizable person re- identification with query-adaptive convolution and temporal lifting, in: Proceedings of the European Conference on Computer Vision (ECCV), 2020

    2020

  40. [50]

    C. Finn, P. Abbeel, S. Levine, Model-agnostic meta-learning for fast adaptation of deep networks, in: Proceedings of the International Con- ference on Machine Learning (ICML), 2017

    2017

  41. [51]

    D. Li, Y. Yang, Y. Z. Song, T. Hospedales, Learning to generalize: Meta-learning for domain generalization, in: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2018

    2018

  42. [52]

    Y. Bai, J. Jiao, W. Ce, J. Liu, Y. Lou, X. Feng, L. Y. Duan, Person30k: A dual-meta generalization network for person re-identification, in: 55 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021

    2021

  43. [53]

    H. Ni, J. Song, X. Luo, F. Zheng, W. Li, H. T. Shen, Meta distribution alignment for generalizable person re-identification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion (CVPR), 2022

    2022

  44. [54]

    S. I. Nikolenko, Synthetic Data for Deep Learning, 2021

    2021

  45. [55]

    Delussu, L

    R. Delussu, L. Putzu, G. Fumera, Synthetic data for video surveillance applications of computer vision: A review, International Journal of Computer Vision (2024)

    2024

  46. [56]

    H. Chen, Z. Wu, Y. Zhong, H. Wang, J. Lai, Learning invariance from generated variance for unsupervised person re-identification, IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)

    2022

  47. [57]

    Putzu, R

    L. Putzu, R. Delussu, G. Fumera, Specialise to generalise: The person re-identification case, in: International Conference on Image Analysis and Processing (ICIAP), 2023

    2023

  48. [58]

    S. Bak, P. Carr, J.-F. Lalonde, Domain adaptation through synthesis for unsupervised person re-identification, in: Proceedings of the Euro- pean Conference on Computer Vision (ECCV), 2018

    2018

  49. [59]

    X. Sun, L. Zheng, Dissecting person re-identification from the view- point of viewpoint, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019

    2019

  50. [60]

    Y. Wang, X. Liang, S. Liao, Cloning outfits from real-world images to 3d characters for generalizable person re-identification, in: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

    2022

  51. [61]

    Chang, Modeling, UV Mapping, and Texturing 3D Game Weapons, Wordware Publishing, Inc., 2006

    C. Chang, Modeling, UV Mapping, and Texturing 3D Game Weapons, Wordware Publishing, Inc., 2006

    2006

  52. [62]

    N. Xu, L. Yang, Y. Fan, D. Yue, Y. Liang, J. Yang, T. Huang, Youtube-vos: A large-scale video object segmentation benchmark, arXiv preprint arXiv:1809.03327 (2018). 56

    2018 arXiv

  53. [63]

    Volpi, H

    R. Volpi, H. Namkoong, O. Sener, J. C. Duchi, V. Murino, S. Savarese, Generalizing to unseen domains via adversarial data augmentation, in: Advances in Neural Information Processing Systems (NeurIPS), 2018

    2018

  54. [64]

    L. Zhao, T. Liu, X. Peng, D. Metaxas, Maximum-entropy adversar- ial data augmentation for improved generalization and robustness, in: Advances in Neural Information Processing Systems (NeurIPS), 2020

    2020

  55. [65]

    Zhong, Y

    Z. Zhong, Y. Zhao, G. H. Lee, N. Sebe, Adversarial style augmenta- tion for domain generalized urban-scene segmentation, in: Advances in Neural Information Processing Systems (NeurIPS), 2022

    2022

  56. [67]

    T. Ma, Y. Sun, Z. Yang, Y. Yang, Prod: Prompting-to-disentangle domain knowledge for cross-domain few-shot image classification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  57. [68]

    Z. Zhou, Y. Lei, B. Zhang, L. Liu, Y. Liu, Zegclip: Towards adapt- ing clip for zero-shot semantic segmentation, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  58. [69]

    S. Li, L. Sun, Q. Li, Clip-reid: Exploiting vision-language model for image re-identification without concrete text labels, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2023)

    2023

  59. [70]

    T. Zhao, X. Wu, Pyramid feature attention network for saliency de- tection, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019

    2019

  60. [71]

    F. Yang, Z. Zhong, Z. Luo, Y. He, S. Li, N. Sebe, Diversity-authenticity co-constrained stylization for federated domain generalization in per- son re-identification, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2024). 57

    2024

  61. [72]

    R. He, X. Wu, Z. Sun, T. Tan, Wasserstein cnn: Learning invariant fea- tures for nir-vis face recognition, IEEE Transactions on Pattern Anal- ysis and Machine Intelligence (2018)

    2018

  62. [73]

    Jiang, T

    K. Jiang, T. Zhang, X. Liu, B. Qian, Y. Zhang, F. Wu, Cross-modality transformerforvisible-infraredpersonre-identification, in: Proceedings of the European Conference on Computer Vision (ECCV), 2022

    2022

  63. [74]

    J. Wang, Z. Zhang, M. Chen, Y. Zhang, C. Wang, B. Sheng, Y. Qu, Y. Xie, Optimal transport for label-efficient visible-infrared person re- identification, in: Proceedings of the European Conference on Com- puter Vision (ECCV), 2022

    2022

  64. [75]

    B. Yang, J. Chen, M. Ye, Towards grand unified representation learning for unsupervised visible-infrared person re-identification, in: Proceed- ings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  65. [76]

    Z. Wu, M. Ye, Unsupervised visible-infrared person re-identification via progressive graph matching and alternate learning, in: Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  66. [77]

    J. Shi, X. Yin, Y. Chen, Y. Zhang, Z. Zhang, Y. Xie, Y. Qu, Multi-memory matching for unsupervised visible-infrared person re- identification, in: Proceedings of the European Conference on Com- puter Vision (ECCV), 2024

    2024

  67. [78]

    J. Wu, H. Liu, Y. Su, W. Shi, H. Tang, Learning concordant attention via target-aware alignment for visible-infrared person re-identification, in: Proceedings of the IEEE/CVF International Conference on Com- puter Vision (ICCV), 2023

    2023

  68. [79]

    H. Yu, X. Cheng, W. Peng, W. Liu, G. Zhao, Modality unifying net- work for visible-infrared person re-identification, in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  69. [80]

    K. Ren, L. Zhang, Implicit discriminative knowledge learning for visible-infrared person re-identification, in: Proceedings of the 58 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  70. [81]

    W. Li, R. Zhao, X. Wang, Human re-identification with transferred metric learning, in: Proceedings of the Asian Conference on Computer Vision (ACCV), 2012

    2012

  71. [82]

    Girshick, F

    R. Girshick, F. Iandola, T. Darrell, J. Malik, Deformable part models are convolutional neural networks, in: Proceedings of the IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), 2015

    2015

  72. [83]

    T. Xiao, S. Li, B. Wang, L. Lin, X. Wang, Joint detection and iden- tification feature learning for person search, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2017

    2017

  73. [84]

    Zheng, L

    Z. Zheng, L. Zheng, Y. Yang, Unlabeled samples generated by gan improve the person re-identification baseline in vitro, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017

    2017

  74. [85]

    S. Ren, K. He, R. Girshick, J. Sun, Faster r-cnn: Towards real-time object detection with region proposal networks, in: Advances in Neural Information Processing Systems (NeurIPS), 2015

    2015

  75. [86]

    Hirzer, C

    M. Hirzer, C. Beleznai, P. M. Roth, H. Bischof, Person re-identification by descriptive and discriminative classification, in: Image Analysis, 2011

    2011

  76. [87]

    D. Gray, S. Brennan, H. Tao, Evaluating appearance models for recog- nition, reacquisition, and tracking, in: Proceedings of IEEE Interna- tional Workshop on Performance Evaluation for Tracking and Surveil- lance, 2007

    2007

  77. [89]

    C. Loy, C. Liu, S. Gong, Person re-identification by manifold rank- ing, in: Proceedings of the IEEE International Conference on Image Processing (ICIP), 2013. 59

    2013

  78. [90]

    M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, S. C. Hoi, Deep learning for person re-identification: A survey and outlook, IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)

    2021

  79. [91]

    Y. Ding, A. Wang, L. Zhang, Multidimensional semantic disentangle- ment network for clothes-changing person re-identification, in: Pro- ceedings of the International Conference on Multimedia Retrieval (ICMR), 2024

    2024

  80. [92]

    Y.Ding, R.Mao, G.Du, L.Zhang, Clothes-eraser: Clothing-awarecon- trollable disentanglement for clothes-changing person re-identification, Signal, Image and Video Processing (2024)

    2024

  81. [93]

    Y. Ding, Y. Wu, A. Wang, T. Gong, L. Zhang, Disentangled body features for clothing change person re-identification, Multimedia Tools and Applications (2024)

    2024

  82. [94]

    Y. Ding, J. Li, H. Wang, Z. Liu, A. Wang, Attention-enhanced multimodal feature fusion network for clothes-changing person re- identification, Complex & Intelligent Systems (2025)

    2025

  83. [95]

    Zhong, L

    Z. Zhong, L. Zheng, G. Kang, S. Li, Y. Yang, Random erasing data augmentation, in: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2020

    2020

  84. [96]

    E. D. Cubuk, B. Zoph, J. Shlens, Q. V. Le, Randaugment: Practi- cal automated data augmentation with a reduced search space, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020

    2020

  85. [97]

    Zheng, Y

    L. Zheng, Y. Yang, A. G. Hauptmann, Mars: A video benchmark for large-scale person re-identification, in: Proceedings of the European Conference on Computer Vision (ECCV), 2016

    2016

  86. [98]

    Y. Wu, Y. Lin, X. Dong, Y. Yan, W. Ouyang, Y. Yang, Exploit the unknown gradually: One-shot video-based person re-identification by stepwise learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 60

    2018

  87. [99]

    M. Li, X. Zhu, S. Gong, Unsupervised person re-identification by deep learning tracklet association, in: Proceedings of the European Confer- ence on Computer Vision (ECCV), 2018

    2018

  88. [100]

    J. Zuo, Z. Li, Y. Huang, S. Zhang, Z. Chen, X. Zhao, G. Huang, Cross- video identity correlating for person re-identification pre-training, in: Advances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  89. [101]

    Carreira, A

    J. Carreira, A. Zisserman, Quo vadis, action recognition? a new model and the kinetics dataset, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

    2017

  90. [102]

    Feichtenhofer, H

    C. Feichtenhofer, H. Fan, J. Malik, K. He, Slowfast networks for video recognition, in: Proceedings of the IEEE/CVF International Confer- ence on Computer Vision (ICCV), 2019

    2019

  91. [103]

    X. Li, T. Liang, Y. Jin, T. Wang, Y. Li, Camera-aware style separation and contrastive learning for unsupervised person re-identification, in: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), 2022

    2022

  92. [104]

    S. Wang, R. Veldhuis, C. Brune, N. Strisciuglio, What do neural net- works learn in image classification? a frequency shortcut perspective, in: Proceedings of the IEEE/CVF International Conference on Com- puter Vision (ICCV), 2023

    2023

  93. [105]

    J. Wang, R. Du, D. Chang, K. Liang, Z. Ma, Domain generalization via frequency-domain-based feature disentanglement and interaction, in: Proceedings of ACM International Conference on Multimedia (ACM MM), 2022

    2022

  94. [106]

    X.He, J.Hu, Q.Lin, C.Luo, W.Xie, S.Song, M.H.Khan, L.Shen, To- wards combating frequency simplicity-biased learning for domain gen- eralization, arXiv preprint arXiv:2410.16146 (2024)

    2024 arXiv

  95. [107]

    Zhang, Y

    G. Zhang, Y. Zhang, T. Zhang, B. Li, S. Pu, Pha: Patch-wise high- frequency augmentation for transformer-based person re-identification, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 61

    2023

  96. [108]

    Zheng, H

    L. Zheng, H. Zhang, S. Sun, M. Chandraker, Y. Yang, Q. Tian, Person re-identification in the wild, in: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition (CVPR), 2017

    2017

  97. [109]

    Z. Li, D. Miao, Sequential end-to-end network for efficient person search, in: Proceedings of the AAAI Conference on Artificial Intel- ligence (AAAI), 2021. 62

    2021

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.