Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification

Bing Wang; Bin Yang; Cuiqun Chen; Mang Ye; Xiao Wang; Xin Xu

REVIEW 3 major objections 6 minor 242 references

Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification

T0 review · 3 major / 6 minor · reviewed 2026-08-02 · deepseek-v4-flash

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

Pith's one-line read This paper argues that person re-identification has become a single spectrum of cross-modal matching tasks, from visible–infrared to text, sketch, and beyond-visibility sensing, and that a simple dual-path Transformer can serve as a competi

desk verdict Useful survey of cross-modal ReID that overstates its own baseline; the survey half deserves a serious referee, the baseline claim does not. read the letter →

arxiv 2607.14821 v1 pith:VOHE5IA6 submitted 2026-07-16 cs.CV

Xiao Wang , Bing Wang , Bin Yang , Cuiqun Chen , Xin Xu , Mang Ye This is my paper · ORCID

classification cs.CV

keywords personre-identificationcross-modalReIDvisible-infraredtext-imagesketchnon-line-of-sightmodality-invariantrepresentationlearningTransformerbaseline

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

The paper tries to organize the many branches of person re-identification—matching the same person across non-overlapping cameras—into one map. It argues that the field has moved from single-modal RGB matching to cross-modal settings (visible–infrared, text–image, sketch–photo, and signal-based non-line-of-sight cases), and that newer tri-spectral and multi-modal systems belong on the same conceptual spectrum. Its organizing principle is to classify tasks by retrieval protocol and learning objective rather than by the number of modalities involved. The paper also proposes a Transformer-based visible–infrared baseline and claims it outperforms existing methods on the RegDB and SYSU-MM01 benchmarks in both supervised and unsupervised settings. If the map is right, techniques for reducing modality gaps should transfer across all these subfields instead of being developed in isolation.

What carries the argument

The survey's organizing device is a two-axis taxonomy: each person re-identification task is classified by its retrieval protocol (which modality queries which) and by its primary learning objective (heterogeneous alignment versus spectral-aware representation versus multi-source fusion). This places VI, text-image, sketch, NLOS, tri-spectral, and multi-modal work under one framework. The experimental machinery is a dual-path Transformer baseline for VI-ReID: separate patch embeddings for visible and infrared images, a shared Transformer encoder whose class token aggregates identity information, feature-level modality confusion to erase modality-specific cues, a shared memory bank for cluste

What would settle it

Run the proposed baseline, with the paper's own settings, on the low-light LLCM benchmark without adaptation after training on RegDB or SYSU-MM01; if its Rank-1 and mAP fall to the level of a standard CNN baseline under the same protocol, the claim that simple Transformer-based modality confusion yields robust modality-invariant features is not supported.

Watch

Extended reading notes

Core claim

The paper's central claim is that existing person re-identification research can be described for the first time by a single taxonomy: cross-modal tasks (VI-ReID, TI-ReID, Sketch-ReID, NLOS-ReID) share the problem of heterogeneous modality alignment; tri-spectral ReID centers on spectral-aware representation; and multi-modal ReID centers on multi-source fusion. The authors further claim that a simple Transformer-based framework—separate patch embeddings for visible and infrared inputs, a shared Transformer encoder with a class token as an identity aggregator, feature-level modality confusion, a shared memory bank, and cluster-contrastive learning—captures these principles and achieves strong

Load-bearing premise

The load-bearing premise for the experimental contribution is that single-run, within-dataset Rank-1 and mAP on RegDB and SYSU-MM01 are sufficient evidence that the proposed Transformer baseline is superior—a premise the paper itself questions in Section 2.1.1, where it calls for cross-dataset and camera-disjoint evaluation rather than reliance on within-dataset numbers alone.

Editorial extensions

If this is right

If the taxonomy is right, a method's worth in cross-modal ReID should be judged by how well it solves heterogeneous alignment rather than by which pair of modalities it handles, encouraging technique transfer across VI, text-image, sketch, and NLOS settings.
The proposed baseline demonstrates that a Transformer with a shared encoder, class token, and feature-level modality confusion can outperform established CNN-based VI-ReID methods on RegDB and SYSU-MM01 in both supervised and unsupervised settings.
IR-guided RGB pseudo-label refinement improves unsupervised performance, supporting the principle that the more stable modality can be used to supervise pseudo-label generation for the noisier one.
On the five-modality ORBench-style protocol, adding infrared and color-pencil queries to a text query yields large mAP gains, while adding sketch to an already rich combination gives only marginal gains, so sensor selection should account for diminishing returns.
The paper's own recommendation that future work report cross-dataset, camera-disjoint, and missing-modality evaluation implies that current within-dataset Rank-1/mAP numbers should be read cautiously.

Reading between the lines

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

Editorial inference: if the taxonomy is adopted, benchmark design could move toward a single multi-modal gallery (RGB, IR, text, sketch, and NLOS-style queries) so that alignment techniques are compared under one protocol instead of separate per-modality-pair datasets.
Editorial inference: a testable extension of the baseline is to train on RegDB and evaluate on the low-light LLCM set without adaptation; if the modality-confusion design is genuinely modality-invariant, the drop should be small, and if not, the paper's own recommended cross-dataset protocol would expose it.
Editorial inference: the paper's future direction on causal representation learning implies that current disentanglement methods, which separate factors without causal structure, may not transfer to unseen sensors; a concrete test would swap sensor type at test time and measure the drop.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Here's my take on 2607.14821. The survey part is genuinely useful. As far as I can tell, it is the first to put VI, TI, sketch, and NLOS ReID in one frame, and the organizing principle—retrieval protocol plus learning objective rather than just modality count—is sensible. The NLOS coverage is a nice service to the community, and the tri-spectral and multi-modal fusion discussion is more than a placeholder. If someone is new to cross-modal ReID, this paper will save them a lot of literature hunting. I would use it as a reference for the landscape.

The baseline, though, is where the paper trips. The text says the proposed Transformer "demonstrates superior overall performance" on SYSU-MM01 and RegDB. That is contradicted by the paper's own Tables 3 and 4. On SYSU-MM01 supervised, DEN gets 76.36/71.38 All R-1/mAP and 83.56/84.65 Indoor; the proposed baseline gets 69.93/68.91 and 76.07/81.50. PartMix and PMCM also beat it. On unsupervised, SDCL and MCL beat it on SYSU-MM01 (e.g., SDCL All R-1 64.49 vs. 60.87). Table 12 simply omits those methods. So the "superior" claim is not just unsubstantiated—it is contradicted by the paper's own numbers. At best the baseline is competitive on RegDB and below state of the art on SYSU. And for a paper that rightly tells the field to move toward cross-dataset and camera-disjoint evaluation, offering only single-run within-dataset results, in a table that omits the stronger comparators, is an inconsistency. No code, no error bars.

Also some editorial issues: the future directions section repeats itself verbatim, and there are typos like "ldentification" in the title. Minor, but worth cleaning.

I would not call this fatal for the survey. The taxonomy and coverage are the contribution, and they hold up. The baseline can be reframed as a modest reference model—simple and reproducible in principle—without claiming superiority. The authors should fix the claim, add the missing comparisons, and ideally release code and report variance.

Verdict: send it to peer review as a survey, but expect the experimental section to be revised. I'd be fine citing the survey.

Referee Report

3 major / 6 minor

Summary. The paper surveys person re-identification from single-modal to cross-modal and multi-modal settings. It organizes VI-ReID, TI-ReID, Sketch-ReID, NLOS-ReID, tri-spectral ReID, and multi-modal ReID under a taxonomy based on retrieval protocol and learning objective, and reviews datasets and representative methods. As a second contribution, it proposes a Transformer-based VI-ReID baseline with supervised and unsupervised variants, evaluated on SYSU-MM01 and RegDB. The paper claims to be the first survey covering this combination of scenarios.

Significance. If the survey's coverage claim holds, the paper provides a useful structured reference: it brings together six task families that are usually treated separately, includes extensive method and dataset tables, and is candid about benchmark-specific biases and the need for stronger evaluation protocols. The proposed taxonomy is a reasonable organizing principle. The baseline contribution, however, is not established as claimed: the experimental evidence in the paper's own Tables 3 and 4 contradicts the 'superior overall performance' statement, and no code or error bars are provided. The survey content remains defensible, but the experimental claim requires substantial revision.

major comments (3)

[§5 / Table 12 vs Tables 3 and 4] The claim in §5 that the proposed baseline 'demonstrates superior overall performance on both the RegDB and SYSU-MM01 datasets' under supervised and unsupervised settings is contradicted by the paper's own tables. In the supervised setting, DEN [54] (Table 3) outperforms the baseline on every reported metric: SYSU-MM01 All 76.36/71.38 vs 69.93/68.91, Indoor 83.56/84.65 vs 76.07/81.50, RegDB V→I 95.34/90.21 vs 93.48/88.72, and I→V 94.98/90.24 vs 92.61/87.72. PartMix [55] also beats the baseline on SYSU-MM01 All and Indoor. In the unsupervised setting, SDCL [99] (Table 4) exceeds the baseline on SYSU-MM01 All (64.49/63.24 vs 60.87/59.56) and Indoor (71.37/76.90 vs 66.13/73.21). None of these methods appears in Table 12. The comparison is therefore selective, and the 'superior overall' statement should be replaced with a qualified claim or the table should include the full set of methods fr
[§5 / §2.1.1 Evaluation protocol] The experimental support for the baseline is limited to single-run within-dataset Rank-1 and mAP on SYSU-MM01 and RegDB. No cross-dataset evaluation, camera-disjoint testing, or robustness to degraded modalities is reported, despite §2.1.1 explicitly recommending that 'future studies should report cross-dataset evaluation, camera- or environment-disjoint testing' rather than relying solely on within-dataset Rank-1 and mAP. The absence of error bars or multiple-seed results further weakens the 'superior' and 'significantly surpass' wording. These limitations are structurally separate from the survey's coverage contribution and should be fixed by strengthening the evaluation or by re-scoping the claim.
[§5.2 Ablation study] The ablations in Table 13 report single-run numbers without variance or statistical tests. Since the reported gaps among some variants are small (e.g., supervised SYSU R-1 69.93 vs 67.21), it is not possible to determine whether the differences are meaningful. In addition, the ablation does not include a standard Transformer ReID baseline (e.g., DC-Former [238], which the design explicitly draws on), so the incremental contribution of the proposed modules is not isolated from the gains of the backbone. Please provide repeated runs, variance, or a stronger baseline comparison.

minor comments (6)

[§6] The paragraph 'Identity-aware Foundation Models' appears twice verbatim in the Future Research section; remove the duplicate.
[§2.4] The Wi-PER81 dataset is introduced twice through the same reference [214]; the later 'More recently, Cascio et al. [214] further advanced' sentence should be merged with the earlier description.
[Table 6] For PD [160], the RSTPReid mAP is listed as '????'; please supply the value or mark it as not reported consistently with other entries.
[Table 2] The header 'Identitiesr' contains a typo; should be 'Identities'. Also the table caption says 'Low-light' but the column is not defined in the main text.
[§2.2] The text refers to 'Fig. 1, D' when discussing noisy correspondence in TI-ReID; the intended figure appears to be Fig. 5. Please correct the cross-reference.
[§2.4] The NLOS ReID section would benefit from a performance summary table analogous to Tables 3, 4, 6, 8, 9, and 11; currently the relative strengths of ReID3D, mmWave, and RF methods are described only qualitatively.

Circularity Check

0 steps flagged · score 1.0 of 10

No load-bearing circularity; survey coverage claim is independent and the baseline is externally evaluated, although its 'superior overall performance' statement is inconsistent with the paper's own tables.

full rationale

The paper's primary contribution is a survey and taxonomy, not a derivation. The 'first survey' claim is supported by comparison with prior surveys in Table 1 and is not justified by self-citation. The proposed Transformer baseline is trained and evaluated on external benchmarks SYSU-MM01 and RegDB; its components are borrowed or described (random channel augmentation from [50], class token from DC-Former [238], cluster contrast learning, IR-guided pseudo-label refinement), and the reported numbers are not fitted to the claims. The main weakness is evaluative, not circular: Section 2.1.1 states that 'future studies should report cross-dataset evaluation, camera- or environment-disjoint testing... rather than relying solely on within-dataset Rank-1 accuracy and mAP,' while Section 5 claims 'superior overall performance' based only on within-dataset results. Moreover, Table 12 omits stronger methods from Tables 3 and 4 (e.g., DEN, PartMix, PMCM, SDCL, MCL), so the 'superior' claim is unsupported or selective. This is a correctness/reporting concern, not a reduction of a prediction to its inputs by construction. Self-citations by the author group appear in baseline design, but they are not used to forbid alternatives or to establish a uniqueness result; the baseline remains checkable against public data. Therefore the circularity score is low.

Assumptions & free parameters 9 free parameters · 4 assumptions · 0 invented entities

The survey itself introduces no free parameters; the free parameters listed belong to the proposed baseline and are hand-set validation hyperparameters. The baseline relies on the domain assumptions stated in Section 5 (Transformer superiority, IR label stability) and the representativeness of the two benchmarks. No new physical or conceptual entities (e.g., new sensors, new features) are invented beyond the architectural components.

free parameters (9)

DBSCAN threshold = 0.6
Used for pseudo-label clustering in unsupervised VI-ReID; chosen by the authors, no sensitivity analysis reported (Section 5.1).
DBSCAN min_samples = 4
Clustering parameter in the unsupervised pipeline (Section 5.1).
Re-ranking k1, k2 = k1=30, k2=6
Parameters for re-ranked Jaccard distance used in clustering (Section 5.1).
Contrastive temperature = 0.05
Temperature for memory-based contrastive loss (Section 5.1).
Memory momentum = 0.1
Momentum for updating memory banks (Section 5.1).
Initial learning rate = 3.5e-4
SGD learning rate (Section 5.1).
Weight decay = 5e-4
SGD weight decay (Section 5.1).
Learning rate schedule = decay 0.1 every 20 epochs
Learning rate reduction schedule (Section 5.1).
Image size = 288x144
Input resolution for all images (Section 5.1).

assumptions (4)

domain assumption Transformer backbones are better suited than CNNs for cross-modal VI-ReID global modeling
Section 5 motivates the baseline by claiming Transformers are better for global structural and shape information, especially for infrared images.
domain assumption IR modality retains more stable structural information, so IR cluster labels can refine RGB pseudo-labels
Stated in Section 5 as the basis for the unsupervised IR-PR module.
domain assumption The taxonomy based on retrieval protocol and learning objective is a natural grouping of ReID tasks
Section 1 and Fig. 2 propose this organizational scheme; the survey's value depends on this grouping being useful.
domain assumption SYSU-MM01 and RegDB are representative benchmarks for VI-ReID evaluation
The baseline is evaluated only on these two datasets; the paper itself calls for cross-dataset evaluation in Section 2.1.1 but does not perform it.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification." pith.science (2026). https://pith.science/paper/VOHE5IA6

@misc{pith2026260714821,
  author       = {Pith},
  title        = {Pith review of: Blurring Modal Boundaries: A Unified Survey from Single- to Multi-Modal Person Re-ldentification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VOHE5IA6}},
  note         = {Machine review of arXiv:2607.14821}
}

read the original abstract

Person re-identification (ReID) serves as a critical component in intelligent surveillance systems, aiming to match identities across disjoint camera networks. While traditional methods primarily rely on single-modal RGB imagery, they are often constrained by environmental challenges such as low illumination and occlusion. To overcome these limitations, the field is rapidly evolving toward cross-modal and multi-modal paradigms. This survey presents a comprehensive overview of this transition, systematically reviewing key cross-modal tasks including visible-infrared (VI-ReID), text-image (TI-ReID), sketch-based (Sketch-ReID), and the emerging Non-Line-of-Sight (NLOS) ReID, which extends perception beyond direct visibility. Furthermore, we examine tri-spectral and multi-modal fusion ReID, discussing how complementary information from diverse sensors enhances robustness. Beyond summarizing datasets, challenges, and methodologies, we propose a Transformer-based baseline framework for visible-infrared ReID, designed to effectively capture modality-invariant features. Finally, based on the current landscape, we outline several promising directions for future research.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

242 extracted references · 21 linked inside Pith

[54]

In: Winter Conference on Applications of Computer Vision (WACV), pp

Kim, S., Gwon, S., Seo, K.: Enhancing diverse intra-identity representation for visible-infrared person re-identification. In: Winter Conference on Applications of Computer Vision (WACV), pp. 2513–2522 (2024)

2024
[55]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Kim, M., Kim, S., Park, J., Park, S., Sohn, K.: Partmix: Regularization strat- egy to learn part discovery for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18621–18632 (2023)

2023
[99]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Yang, B., Chen, J., Ye, M.: Shallow-deep collaborative learning for unsuper- vised visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16870–16879 (2024)

2024
[238]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Li, W., Zou, C., Wang, M., Xu, F., Zhao, J., Zheng, R., Cheng, Y., Chu, W.: Dc-former: Diverse and compact transformer for person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 1415– 1423 (2023)

2023
[1]

Cao, C., Fu, X., Xu, S., Ge, C., Wang, K., Zha, Z.: Learning robust event- guided representations for person re-identification. Int. J. Comput. Vis.134(2), 82 (2026)

2026
[2]

Dai, Y., Sun, Y., Liu, J., Tong, Z., Duan, L.: Bridging the source-to-target gap for cross-domain person re-identification with intermediate domains. Int. J. Comput. Vis.133(1), 410–434 (2025)

2025
[3]

Zhang, G., Tan, S., Ji, Z., Fang, Y.: Dynamic attention vision-language trans- former network for person re-identification. Int. J. Comput. Vis.133(4), 1927–1939 (2025)

1927
[4]

arXiv preprint arXiv:1610.02984 (2016)

Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984 (2016)

arXiv 2016

Show all 242 references

[5]

In: International Conference on Neural Information Processing, pp

Wieczorek, M., Rychalska, B., Dąbrowski, J.: On the unreasonable effective- ness of centroids in image retrieval. In: International Conference on Neural Information Processing, pp. 212–223 (2021). Springer

2021
[6]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Wang, G., Lai, J., Huang, P., Xie, X.: Spatial-temporal person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8933–8940 (2019)

2019
[7]

Journal of Big Data10(1), 74 (2023)

Alnissany, A., Dayoub, Y.: Modified centroid triplet loss for person re- identification. Journal of Big Data10(1), 74 (2023)

2023
[8]

Image and Vision Computing116, 104330 (2021)

Zang, X., Li, G., Gao, W., Shu, X.: Learning to disentangle scenes for person re-identification. Image and Vision Computing116, 104330 (2021)

2021
[9]

In: ICASSP 2023- 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp

Shi, X., Liu, H., Shi, W., Zhou, Z., Li, Y.: Boosting person re-identification 40 with viewpoint contrastive learning and adversarial training. In: ICASSP 2023- 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5 (2023). IEEE

2023
[10]

In: Advances in Neural Information Processing Systems, vol

Gong, Y., Zhong, Z., Qu, Y., Luo, Z., Ji, R., Jiang, M.: Cross-modality per- turbation synergy attack for person re-identification. In: Advances in Neural Information Processing Systems, vol. 37, pp. 23352–23377 (2024)

2024
[11]

In: Proceedings of the IEEE International Conference on Computer Vision, pp

Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable per- son re-identification: A benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)

2015
[12]

In: 2012 19th IEEE International Conference on Image Processing, pp

D’Orazio, T., Cicirelli, G.: People re-identification and tracking from multi- ple cameras: A review. In: 2012 19th IEEE International Conference on Image Processing, pp. 1601–1604 (2012). IEEE

2012
[13]

Image and vision computing32(4), 270–286 (2014)

Bedagkar-Gala, A., Shah, S.K.: A survey of approaches and trends in person re-identification. Image and vision computing32(4), 270–286 (2014)

2014
[14]

arXiv preprint arXiv:1307.5748 (2013)

Satta, R.: Appearance descriptors for person re-identification: a comprehensive review. arXiv preprint arXiv:1307.5748 (2013)

2013 arXiv
[15]

IEEE Transactions on Circuits and Systems for Video Technology30(4), 1092– 1108 (2019)

Leng, Q., Ye, M., Tian, Q.: A survey of open-world person re-identification. IEEE Transactions on Circuits and Systems for Video Technology30(4), 1092– 1108 (2019)

2019
[16]

IEEE Transactions on Pattern Analysis and Machine Intelligence44(6), 2872–2893 (2022) https://doi.org/10

Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.H.: Deep learning for person re-identification: A survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence44(6), 2872–2893 (2022) https://doi.org/10. 1109/TPAMI.2021.3054775

2022
[17]

Electronics 11(3), 454 (2022)

Zheng, H., Zhong, X., Huang, W., Jiang, K., Liu, W., Wang, Z.: Visible-infrared person re-identification: A comprehensive survey and a new setting. Electronics 11(3), 454 (2022)

2022
[18]

Multimedia Tools and Applications83(42), 90243–90270 (2024)

Chang, H., Xu, X., Liu, W., Lu, L., Li, W.: A comprehensive survey of visible infrared person re-identification from an application perspective. Multimedia Tools and Applications83(42), 90243–90270 (2024)

2024
[19]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Nguyen, V.D., Mirza, S., Zakeri, A., Gupta, A., Khaldi, K., Aloui, R., Mantini, P., Shah, S.K., Merchant, F.: Tackling domain shifts in person re-identification: A survey and analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4...

2024
[20]

Mathematics12(16), 2495 (2024) 41

Chen, Y., Wang, K., Ye, H., Tao, L., Tie, Z.: Person re-identification in special scenes based on deep learning: A comprehensive survey. Mathematics12(16), 2495 (2024) 41

2024
[21]

arXiv preprint arXiv:1905.10048 (2019)

Wang, Z., Wang, Z., Zheng, Y., Wu, Y., Zeng, W., Satoh, S.: Beyond intra- modality: A survey of heterogeneous person re-identification. arXiv preprint arXiv:1905.10048 (2019)

1905 arXiv
[22]

Infor- mation Fusion91, 396–411 (2023)

Huang, N., Liu, J., Miao, Y., Zhang, Q., Han, J.: Deep learning for visible- infrared cross-modality person re-identification: A comprehensive review. Infor- mation Fusion91, 396–411 (2023)

2023
[23]

In: Visual Analysis of Behaviour: From Pixels to Semantics, pp

Gong, S., Xiang, T.: Person re-identification. In: Visual Analysis of Behaviour: From Pixels to Semantics, pp. 301–313. Springer, ??? (2014)

2014
[24]

In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)

Wu, A., Zheng, W.-S., Yu, H.-X., Gong, S., Lai, J.: Rgb-infrared cross-modality person re-identification. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)

2017
[25]

Sensors17(3), 605 (2017)

Nguyen, D.T., Hong, H.G., Kim, K.W., Park, K.R.: Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors17(3), 605 (2017)

2017
[26]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Zhang, Y., Wang, H.: Diverse embedding expansion network and low-light cross- modality benchmark for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2153–2162 (2023)

2023
[27]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmen- tation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13001–13008 (2020)

2020
[28]

In: Proceedings of the European Confer- ence on Computer Vision (ECCV) Workshops, pp

Kniaz, V.V., Knyaz, V.A., Hladuvka, J., Kropatsch, W.G., Mizginov, V.: Thermalgan: Multimodal color-to-thermal image translation for person re- identification in multispectral dataset. In: Proceedings of the European Confer- ence on Computer Vision (ECCV) Workshops, pp. 1–10 (2018)

2018
[29]

In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp

Zhong, X., Lu, T., Huang, W., Yuan, J., Liu, W., Lin, C.-W.: Visible-infrared person re-identification via colorization-based siamese generative adversarial network. In: Proceedings of the 2020 International Conference on Multimedia Retrieval, pp. 421–427 (2020)

2020
[30]

In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Wang, Z., Wang, Z., Zheng, Y., Chuang, Y.-Y., Satoh, S.: Learning to reduce dual-level discrepancy for infrared-visible person re-identification. In: Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 618–626 (2019)

2019
[31]

In: Proceedings of the IEEE International Conference on Computer Vision, pp

Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image transla- tion using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017) 42

2017
[32]

IEEE Transactions on Circuits and Systems for Video Technology34(1), 518–533 (2023)

Qi, J., Liang, T., Liu, W., Li, Y., Jin, Y.: A generative-based image fusion strat- egy for visible-infrared person re-identification. IEEE Transactions on Circuits and Systems for Video Technology34(1), 518–533 (2023)

2023
[33]

IEEE Signal Processing Letters31, 346–350 (2024)

Qian, Y., Tang, S.-K.: Pose attention-guided paired-images generation for visible-infrared person re-identification. IEEE Signal Processing Letters31, 346–350 (2024)

2024
[34]

In: Proceedings of the 29th ACM International Conference on Multimedia, pp

Zhang, Y., Yan, Y., Lu, Y., Wang, H.: Towards a unified middle modality learn- ing for visible-infrared person re-identification. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 788–796 (2021)

2021
[35]

Huang, Z., Liu, J., Li, L., Zheng, K., Zha, Z.-J.: Modality-adaptive mixup and invariantdecompositionforrgb-infraredpersonre-identification.In:Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 1034–1042 (2022)

2022
[36]

Pattern Recognition157, 110873 (2025)

Qian, Z., Lin, Y., Du, B.: Visible–infrared person re-identification via patch- mixed cross-modality learning. Pattern Recognition157, 110873 (2025)

2025
[37]

In: IJCAI, vol

Ye, M., Wang, Z., Lan, X., Yuen, P.C.: Visible thermal person re-identification via dual-constrained top-ranking. In: IJCAI, vol. 1, p. 2 (2018)

2018
[38]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Choi,S.,Lee,S.,Kim,Y.,Kim,T.,Kim,C.:Hi-cmd:Hierarchicalcross-modality disentanglement for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10257–10266 (2020)

2020
[39]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Wang, G.-A., Zhang, T., Yang, Y., Cheng, J., Chang, J., Liang, X., Hou, Z.-G.: Cross-modality paired-images generation for rgb-infrared person re- identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12144–12151 (2020)

2020
[40]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Wang, G., Zhang, T., Cheng, J., Liu, S., Yang, Y., Hou, Z.: Rgb-infrared cross-modality person re-identification via joint pixel and feature alignment. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3623–3632 (2019)

2019
[41]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Li, D., Wei, X., Hong, X., Gong, Y.: Infrared-visible cross-modal person re- identification with an x modality. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 4610–4617 (2020)

2020
[42]

Crandall, D., Shao, L., Luo, J.: Dynamic dual-attentive aggregation learning for visible-infrared person re-identification

Ye, M., Shen, J., J. Crandall, D., Shao, L., Luo, J.: Dynamic dual-attentive aggregation learning for visible-infrared person re-identification. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII 16, pp. 229–247 (20...

2020
[43]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Park, H., Lee, S., Lee, J., Ham, B.: Learning by aligning: Visible-infrared per- son re-identification using cross-modal correspondences. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12046–12055 (2021)

2021
[44]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Chen, Y., Wan, L., Li, Z., Jing, Q., Sun, Z.: Neural feature search for rgb- infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 587–597 (2021)

2021
[45]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Fu, C., Hu, Y., Wu, X., Shi, H., Mei, T., He, R.: Cm-nas: Cross-modality neural architecture search for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11823– 11832 (2021)

2021
[46]

In: International Conference on Computer Vision, pp

Hao, X., Zhao, S., Ye, M., Shen, J.: Cross-modality person re-identification via modality confusion and center aggregation. In: International Conference on Computer Vision, pp. 16403–16412 (2021)

2021
[47]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Zhang, Q., Lai, C., Liu, J., Huang, N., Han, J.: Fmcnet: Feature-level modality compensation for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7349– 7358 (2022)

2022
[48]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Wei, Z., Yang, X., Wang, N., Gao, X.: Syncretic modality collaborative learning for visible infrared person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 225–234 (2021)

2021
[49]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Yang, M., Huang, Z., Hu, P., Li, T., Lv, J., Peng, X.: Learning with twin noisy labels for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14308–14317 (2022)

2022
[50]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Ye, M., Ruan, W., Du, B., Shou, M.Z.: Channel augmented joint learning for visible-infrared recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 13567–13576 (2021)

2021
[51]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Wu, Q., Dai, P., Chen, J., Lin, C.-W., Wu, Y., Huang, F., Zhong, B., Ji, R.: Discover cross-modality nuances for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4330–4339 (2021)

2021
[52]

In: Proceedings of the 30th ACM International Conference on Multimedia, pp

Sun, H., Liu, J., Zhang, Z., Wang, C., Qu, Y., Xie, Y., Ma, L.: Not all pixels are matched: Dense contrastive learning for cross-modality person re-identification. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 5333–5341 (2022) 44

2022
[53]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Liu, J., Sun, Y., Zhu, F., Pei, H., Yang, Y., Li, W.: Learning memory- augmented unidirectional metrics for cross-modality person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19366–19375 (2022)

2022
[56]

IEEE Transactions on Information Forensics and Security16, 728–739 (2020)

Ye, M., Shen, J., Shao, L.: Visible-infrared person re-identification via homo- geneous augmented tri-modal learning. IEEE Transactions on Information Forensics and Security16, 728–739 (2020)

2020
[57]

IEEE Transactions on neural networks and learning systems34(4), 1958–1971 (2021)

Liu, H., Ma, S., Xia, D.: Sfanet: A spectrum-aware feature augmentation net- work for visible-infrared person reidentification. IEEE Transactions on neural networks and learning systems34(4), 1958–1971 (2021)

1958
[58]

IEEE Transactions on Circuits and Systems for Video Technology (2024)

Zhang, H., Cheng, S., Du, A.: Multi-stage auxiliary learning for visible-infrared person re-identification. IEEE Transactions on Circuits and Systems for Video Technology (2024)

2024
[59]

IEEE Transactions on Pattern Analysis and Machine Intelligence 46(4), 2299–2315 (2023)

Ye, M., Wu, Z., Chen, C., Du, B.: Channel augmentation for visible-infrared re- identification. IEEE Transactions on Pattern Analysis and Machine Intelligence 46(4), 2299–2315 (2023)

2023
[60]

IEEE Transactions on Information Forensics and Security18, 1554–1565 (2022)

Zhang, Y., Kang, Y., Zhao, S., Shen, J.: Dual-semantic consistency learning for visible-infrared person re-identification. IEEE Transactions on Information Forensics and Security18, 1554–1565 (2022)

2022
[61]

arXiv preprint arXiv:2003.00213 (2020)

Fan, X., Luo, H., Zhang, C., Jiang, W.: Cross-spectrum dual-subspace pair- ing for rgb-infrared cross-modality person re-identification. arXiv preprint arXiv:2003.00213 (2020)

2003 arXiv
[62]

arXiv preprint arXiv:2411.01225 (2024)

Tan, L., Zhang, Y., Han, K., Dai, P., Zhang, Y., Wu, Y., Ji, R.: Rle: A uni- fied perspective of data augmentation for cross-spectral re-identification. arXiv preprint arXiv:2411.01225 (2024)

2024 arXiv
[63]

IEEE Transactions on Information Forensics and Security (2025) 45

Alehdaghi, M., Josi, A., Cruz, R.M., Shamsolameli, P., Granger, E.: Adaptive generation of privileged intermediate information for visible-infrared person re- identification. IEEE Transactions on Information Forensics and Security (2025) 45

2025
[64]

In: Proceedings of the 28th ACM International Conference on Multimedia, pp

Pu, N., Chen, W., Liu, Y., Bakker, E.M., Lew, M.S.: Dual gaussian-based vari- ational subspace disentanglement for visible-infrared person re-identification. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2149–2158 (2020)

2020
[65]

In: IEEE International Conference on Multimedia and Expo (ICME), pp

Li, X., Liang, T., Jin, Y., Wang, T., Li, Y.: Camera-aware style separation and contrastive learning for unsupervised person re-identification. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2022). IEEE

2022
[66]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Feng, J., Wu, A., Zheng, W.-S.: Shape-erased feature learning for visible- infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22752–22761 (2023)

2023
[67]

Pattern Recognition135, 109145 (2023)

Huang, N., Liu, J., Luo, Y., Zhang, Q., Han, J.: Exploring modality-shared appearance features and modality-invariant relation features for cross-modality person re-identification. Pattern Recognition135, 109145 (2023)

2023
[68]

Electronics13(14), 2825 (2024)

Zhou, F., Cheng, Z., Yang, H., Song, Y., Fu, S.: Progressive discriminative feature learning for visible-infrared person re-identification. Electronics13(14), 2825 (2024)

2024
[69]

In: IJCAI, vol

Dai, P., Ji, R., Wang, H., Wu, Q., Huang, Y.: Cross-modality person re- identification with generative adversarial training. In: IJCAI, vol. 1, p. 6 (2018)

2018
[70]

IEEE Transactions on Image Processing29, 579–590 (2019)

Feng, Z., Lai, J., Xie, X.: Learning modality-specific representations for visible- infrared person re-identification. IEEE Transactions on Image Processing29, 579–590 (2019)

2019
[71]

IEEE transactions on information forensics and security15, 407–419 (2019)

Ye, M., Lan, X., Wang, Z., Yuen, P.C.: Bi-directional center-constrained top- ranking for visible thermal person re-identification. IEEE transactions on information forensics and security15, 407–419 (2019)

2019
[72]

Neurocomputing386, 97–109 (2020)

Zhu, Y., Yang, Z., Wang, L., Zhao, S., Hu, X., Tao, D.: Hetero-center loss for cross-modality person re-identification. Neurocomputing386, 97–109 (2020)

2020
[73]

IEEE Transactions on neural networks and learning systems (2021)

Zhang, L., Du, G., Liu, F., Tu, H.: Global-local multiple granularity learning for cross-modality visible-infrared person reidentification. IEEE Transactions on neural networks and learning systems (2021)

2021
[74]

In: European Conference on Computer Vision, pp

Zhang, Y., Zhao, S., Kang, Y.: Modality synergy complement learning with cascaded aggregation for visible-infrared person re-identification. In: European Conference on Computer Vision, pp. 462–479 (2022). Springer

2022
[75]

In: Proceedings of the IEEE/CVF 46 International Conference on Computer Vision, pp

Yu, H., Cheng, X., Peng, W., Liu, W., Zhao, G.: Modality unifying network for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF 46 International Conference on Computer Vision, pp. 11185–11195 (2023)

2023
[76]

arXiv preprint arXiv:2110.08994 (2021)

Liang, T., Jin, Y., Gao, Y., Liu, W., Feng, S., Wang, T., Li, Y.: Cmtr: Cross- modality transformer for visible-infrared person re-identification. arXiv preprint arXiv:2110.08994 (2021)

2021 arXiv
[77]

IEEE Transactions on multimedia 25, 3668–3680 (2022)

Zhao, J., Wang, H., Zhou, Y., Yao, R.: Spatial-channel enhanced transformer for visible-infrared person re-identification. IEEE Transactions on multimedia 25, 3668–3680 (2022)

2022
[78]

IEEE Transactions on Circuits and Systems for Video Technology33(11), 6764–6776 (2023)

Chai, Z., Ling, Y., Luo, Z., Lin, D., Jiang, M., Li, S.: Dual-stream transformer with distribution alignment for visible-infrared person re-identification. IEEE Transactions on Circuits and Systems for Video Technology33(11), 6764–6776 (2023)

2023
[79]

IEEE Transactions on Instrumentation and Measurement73, 1–10 (2024)

Liu, M., Sun, Y., Wang, X., Bian, Y.: Pose-guided modality-invariant feature alignment for visible–infrared object re-identification. IEEE Transactions on Instrumentation and Measurement73, 1–10 (2024)

2024
[80]

Neurocomputing556, 126652 (2023)

Miao, Y., Huang, N., Ma, X., Zhang, Q., Han, J.: On exploring pose estima- tion as an auxiliary learning task for visible–infrared person re-identification. Neurocomputing556, 126652 (2023)

2023
[81]

IEEE Transactions on Neural Networks and Learning Systems33(9), 4676–4687 (2021)

Wei, Z., Yang, X., Wang, N., Gao, X.: Flexible body partition-based adversar- ial learning for visible infrared person re-identification. IEEE Transactions on Neural Networks and Learning Systems33(9), 4676–4687 (2021)

2021
[82]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Wang, Z., Lu, Y., Li, Q., Tao, X., Guo, Y., Gong, M., Liu, T.: Cris: Clip-driven referring image segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11686–11695 (2022)

2022
[83]

IEEE Transactions on Multimedia (2025)

Yu, X., Dong, N., Zhu, L., Peng, H., Tao, D.: Clip-driven semantic discov- ery network for visible-infrared person re-identification. IEEE Transactions on Multimedia (2025)

2025
[84]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Lee, G., Lee, S., Kim, D., Shin, Y., Yoon, Y., Ham, B.: Camera-driven represen- tation learning for unsupervised domain adaptive person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11453–11462 (2023)

2023
[85]

In: 2024 IEEE International Conference on Multimedia and Expo (ICME), pp

Jiang, N., Qiu, Y., Song, W., Liu, J., Shi, Z., Wang, L.: Joint visual-textual reasoning and visible-infrared modality alignment for person re-identification. In: 2024 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2024). IEEE

2024
[86]

403–412 (2017)

Chen, W., Chen, X., Zhang, J., Huang, K.: Beyond triplet loss: a deep quadru- pletnetworkforpersonre-identification.In:ProceedingsoftheIEEEConference 47 on Computer Vision and Pattern Recognition, pp. 403–412 (2017)

2017
[87]

In: Computer vision–ECCV 2016: 14th European Con- ference, Amsterdam, the Netherlands, October 11–14, 2016, Proceedings, Part VII 14, pp

Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: Computer vision–ECCV 2016: 14th European Con- ference, Amsterdam, the Netherlands, October 11–14, 2016, Proceedings, Part VII 14, pp. 499–515 (2016). Springer

2016
[88]

IEEE Transactions on Multimedia23, 4414–4425 (2020)

Liu, H., Tan, X., Zhou, X.: Parameter sharing exploration and hetero-center triplet loss for visible-thermal person re-identification. IEEE Transactions on Multimedia23, 4414–4425 (2020)

2020
[89]

In: Proceed- ings of the AAAI Conference on Artificial Intelligence, vol

Cheng, D., Wang, X., Wang, N., Wang, Z., Wang, X., Gao, X.: Cross-modality person re-identification with memory-based contrastive embedding. In: Proceed- ings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 425–432 (2023)

2023
[90]

arXiv preprint arXiv:2007.01504 (2020)

Jia, M., Zhai, Y., Lu, S., Ma, S., Zhang, J.: A similarity inference met- ric for rgb-infrared cross-modality person re-identification. arXiv preprint arXiv:2007.01504 (2020)

2007 arXiv
[91]

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)18(1s), 1–15 (2022)

Zhang, L., Guo, H., Zhu, K., Qiao, H., Huang, G., Zhang, S., Zhang, H., Sun, J., Wang, J.: Hybrid modality metric learning for visible-infrared person re- identification. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM)18(1s), 1–15 (2022)

2022
[92]

In: Proceedings of the 29th ACM International Conference on Multimedia, pp

Gao, Y., Liang, T., Jin, Y., Gu, X., Liu, W., Li, Y., Lang, C.: Mso: Multi-feature space joint optimization network for rgb-infrared person re-identification. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 5257–5265 (2021)

2021
[93]

IEEE Signal Processing Letters28, 2003–2007 (2021)

Kong, J., He, Q., Jiang, M., Liu, T.: Dynamic center aggregation loss with mixed modality for visible-infrared person re-identification. IEEE Signal Processing Letters28, 2003–2007 (2021)

2003
[94]

Knowledge-Based Systems215, 106772 (2021)

Cai, X., Liu, L., Zhu, L., Zhang, H.: Dual-modality hard mining triplet-center loss for visible infrared person re-identification. Knowledge-Based Systems215, 106772 (2021)

2021
[95]

In: Winter Conference on Applications of Computer Vision, pp

Kim, S., Gwon, S., Seo, K.: Enhancing diverse intra-identity representation for visible-infrared person re-identification. In: Winter Conference on Applications of Computer Vision, pp. 2513–2522 (2024)

2024
[96]

IEEE Transactions on Image Processing30, 6392–6407 (2021) 48

Liang, W., Wang, G., Lai, J., Xie, X.: Homogeneous-to-heterogeneous: Unsu- pervised learning for rgb-infrared person re-identification. IEEE Transactions on Image Processing30, 6392–6407 (2021) 48

2021
[97]

In: ACM International Conference on Multimedia, pp

Yang, B., Ye, M., Chen, J., Wu, Z.: Augmented dual-contrastive aggrega- tion learning for unsupervised visible-infrared person re-identification. In: ACM International Conference on Multimedia, pp. 2843–2851 (2022)

2022
[98]

In: International Conference on Computer Vision, pp

Yang, B., Chen, J., Ye, M.: Towards grand unified representation learn- ing for unsupervised visible-infrared person re-identification. In: International Conference on Computer Vision, pp. 11069–11079 (2023)

2023
[100]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Lin, Y., Dong, X., Zheng, L., Yan, Y., Yang, Y.: A bottom-up clustering approach to unsupervised person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 8738–8745 (2019)

2019
[101]

IEEE Transactions on Circuits and Systems for Video Technology (2023)

Pang, Z., Wang, C., Zhao, L., Liu, Y., Sharma, G.: Cross-modality hierar- chical clustering and refinement for unsupervised visible-infrared person re- identification. IEEE Transactions on Circuits and Systems for Video Technology (2023)

2023
[102]

IEEE Transactions on Knowledge and Data Engineering36(8), 3934–3947 (2024)

Li, Z., Liu, H., Peng, X., Jiang, W.: Inter-intra modality knowledge learning and clustering noise alleviation for unsupervised visible-infrared person re- identification. IEEE Transactions on Knowledge and Data Engineering36(8), 3934–3947 (2024)

2024
[103]

IEEE Transactions on Neural Networks and Learning Systems (2025)

Yang, Y., Hu, W., He, Q.: Dynamic modality–camera-invariant clustering for unsupervised visible–infrared person re-identification. IEEE Transactions on Neural Networks and Learning Systems (2025)

2025
[104]

IEEE Transactions on Circuits and Systems for Video Technology (2025)

Xia, R.-S., Wang, X.-Y., Chen, S.-B., Tang, J., Luo, B.: Camera-proxy enhanced identity-recalibration learning for unsupervised visible-infrared per- son re-identification. IEEE Transactions on Circuits and Systems for Video Technology (2025)

2025
[105]

IEEE Signal Processing Letters (2025)

Wu, R., Yang, Y., He, J., Hu, H.: Extended cross-modality united learning for unsupervised visible-infrared person re-identification. IEEE Signal Processing Letters (2025)

2025
[106]

In: Computer Vision and Pattern Recognition, pp

Wu, Z., Ye, M.: Unsupervised visible-infrared person re-identification via pro- gressive graph matching and alternate learning. In: Computer Vision and Pattern Recognition, pp. 9548–9558 (2023)

2023
[107]

In: European Conference on Computer Vision, pp

Wang, J., Zhang, Z., Chen, M., Zhang, Y., Wang, C., Sheng, B., Qu, Y., Xie, Y.: Optimal transport for label-efficient visible-infrared person re-identification. In: European Conference on Computer Vision, pp. 93–109 (2022). Springer 49

2022
[108]

IEEE Transactions on Information Forensics and Security (2023)

Yang, B., Chen, J., Chen, C., Ye, M.: Dual consistency-constrained learning for unsupervised visible-infrared person re-identification. IEEE Transactions on Information Forensics and Security (2023)

2023
[109]

In: European Conference on Computer Vision, pp

Shi, J., Yin, X., Chen, Y., Zhang, Y., Zhang, Z., Xie, Y., Qu, Y.: Multi-memory matching for unsupervised visible-infrared person re-identification. In: European Conference on Computer Vision, pp. 456–474 (2025). Springer

2025
[110]

In: Proceedings of the 31st ACM International Conference on Multimedia, pp

Cheng, D., He, L., Wang, N., Zhang, S., Wang, Z., Gao, X.: Efficient bilateral cross-modality cluster matching for unsupervised visible-infrared person reid. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 1325–1333 (2023)

2023
[111]

arXiv preprint arXiv:2404.06683 (2024)

Liu, Y., Zhang, W., Vasilakos, A.V., Wang, L.: Unsupervised visible-infrared reid via pseudo-label correction and modality-level alignment. arXiv preprint arXiv:2404.06683 (2024)

2024 arXiv
[112]

IEEE Transactions on Circuits and Systems for Video Technology (2025)

Yin, X., Shi, J., Zhang, Z., Xie, Y., Qu, Y.: Adaptive pseudo-label purification and debiasing for unsupervised visible-infrared person re-identification. IEEE Transactions on Circuits and Systems for Video Technology (2025)

2025
[113]

ACM Transactions on Multimedia Computing, Communications and Applications (2025)

Dai, L., Luo, Z., Ling, Y., Chai, J., Li, S.: Dual-modality-shared learn- ing and label refinement for unsupervised visible-infrared person reid. ACM Transactions on Multimedia Computing, Communications and Applications (2025)

2025
[114]

Knowledge-Based Systems285, 111350 (2024)

Pang, Z., Wang, C., Pan, H., Zhao, L., Wang, J., Guo, M.: Mimr: Modality- invariance modeling and refinement for unsupervised visible-infrared person re- identification. Knowledge-Based Systems285, 111350 (2024)

2024
[115]

Advances in Neural Information Processing Systems37, 99715–99734 (2024)

Shi, J., Yin, X., Zhang, Y., Xie, Y., Qu, Y.,et al.: Learning commonality, divergence and variety for unsupervised visible-infrared person re-identification. Advances in Neural Information Processing Systems37, 99715–99734 (2024)

2024
[116]

In: Proceedings of the 31st ACM International Conference on Multimedia, pp

Cheng, D., Huang, X., Wang, N., He, L., Li, Z., Gao, X.: Unsupervised visible-infrared person reid by collaborative learning with neighbor-guided label refinement. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7085–7093 (2023)

2023
[117]

In: Proceedings of the 32nd ACM International Conference on Multimedia, pp

Yin, X., Shi, J., Zhang, Y., Lu, Y., Zhang, Z., Xie, Y., Qu, Y.: Robust pseudo- label learning with neighbor relation for unsupervised visible-infrared person re-identification. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 2242–2251 (2024)

2024
[118]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Teng, X., Lan, L., Chen, D., Xu, K., Yin, N.: Relieving universal label noise for unsupervised visible-infrared person re-identification by inferring from neigh- bors. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, 50 pp. 7356–7364 (2025)

2025
[119]

IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)

Ye, M., Wu, Z., Du, B.: Dual-level matching with outlier filtering for unsu- pervised visible-infrared person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)

2025
[120]

IEEE Transactions on Information Forensics and Security (2025)

Yang, Y., Hu, W., Hu, H.: Progressive cross-modal association learning for unsupervised visible-infrared person re-identification. IEEE Transactions on Information Forensics and Security (2025)

2025
[121]

In: Proceedings of the 32nd ACM International Conference on Multimedia, pp

Teng, X., Shen, X., Xu, K., Lan, L.: Enhancing unsupervised visible-infrared person re-identification with bidirectional-consistency gradual matching. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 9856–9865 (2024)

2024
[122]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Yao, H., Yang, B., Huang, W., Du, B., Ye, M.: Unsupervised visible-infrared per- son re-identification under unpaired settings. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11916–11926 (2025)

2025
[123]

Wu, A., Lin, C., Zheng, W.-S.: Asymmetric mutual learning for unsupervised transferablevisible-infraredre-identification.IEEETransactionsonCircuitsand Systems for Video Technology (2024)

2024
[124]

In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Li, S., Xiao, T., Li, H., Zhou, B., Yue, D., Wang, X.: Person search with natural language description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1970–1979 (2017)

1970
[125]

In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp

Li, W., Zhao, R., Xiao, T., Wang, X.: Deepreid: Deep filter pairing neural net- work for person re-identification. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 152–159 (2014). https://doi.org/10.1109/CVPR. 2014.27

2014 doi
[126]

arXiv preprint arXiv:1502.02171 (2015)

Zheng, L., Shen, L., Tian, L., Wang, S., Bu, J., Tian, Q.: Person re-identification meets image search. arXiv preprint arXiv:1502.02171 (2015)

2015 arXiv
[127]

In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Xiao, T., Li, S., Wang, B., Lin, L., Wang, X.: Joint detection and identification feature learning for person search. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3415–3424 (2017)

2017
[128]

In: IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol

Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol. 3, pp. 1–7 (2007)

2007
[129]

In: Asian Conference on Computer Vision, pp

Li, W., Zhao, R., Wang, X.: Human reidentification with transferred metric learning. In: Asian Conference on Computer Vision, pp. 31–44 (2012). Springer

2012
[130]

arXiv preprint arXiv:2107.12666

Ding, Z., Ding, C., Shao, Z., Tao, D.: Semantically self-aligned network 51 for text-to-image part-aware person re-identification (2021). arXiv preprint arXiv:2107.12666

2021 arXiv
[131]

In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer gan to bridge domain gap for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 79–88 (2018)

2018
[132]

In: Proceedings of the 29th ACM International Conference on Multimedia, pp

Zhu, A., Wang, Z., Li, Y., Wan, X., Jin, J., Wang, T., Hu, F., Hua, G.: Dssl: Deep surroundings-person separation learning for text-based person retrieval. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 209–217 (2021)

2021
[133]

In: Proceedings of the IEEE International Conference on Computer Vision, pp

Li, S., Xiao, T., Li, H., Yang, W., Wang, X.: Identity-aware textual-visual matching with latent co-attention. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1890–1899 (2017)

2017
[134]

In: Proceedings of the European Conference on Computer Vision (ECCV), pp

Zhang, Y., Lu, H.: Deep cross-modal projection learning for image-text match- ing. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 686–701 (2018)

2018
[135]

In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp

Chen, T., Xu, C., Luo, J.: Improving text-based person search by spatial match- ing and adaptive threshold. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1879–1887 (2018). IEEE

2018
[136]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Ye, M., Lan, X., Li, J., Yuen, P.: Hierarchical discriminative learning for visible thermal person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

2018
[137]

IEEE Transactions on Image Processing30, 4057–4069 (2021)

Chen, Y., Huang, R., Chang, H., Tan, C., Xue, T., Ma, B.: Cross-modal knowl- edge adaptation for language-based person search. IEEE Transactions on Image Processing30, 4057–4069 (2021)

2021
[138]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Wu,Y.,Yan,Z.,Han,X.,Li,G.,Zou,C.,Cui,S.:Lapscore:language-guidedper- son search via color reasoning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1624–1633 (2021)

2021
[139]

IEEE Transactions on Multimedia25, 9315–9329 (2023)

Wu, Z., Ma, B., Chang, H., Shan, S.: Refined knowledge transfer for language- based person search. IEEE Transactions on Multimedia25, 9315–9329 (2023)

2023
[140]

Neurocomputing 494, 171–181 (2022)

Chen, Y., Zhang, G., Lu, Y., Wang, Z., Zheng, Y.: Tipcb: A simple but effective part-based convolutional baseline for text-based person search. Neurocomputing 494, 171–181 (2022)

2022
[141]

arXiv preprint arXiv:2101.03036 (2021) 52

Gao, C., Cai, G., Jiang, X., Zheng, F., Zhang, J., Gong, Y., Peng, P., Guo, X., Sun, X.: Contextual non-local alignment over full-scale representation for text-based person search. arXiv preprint arXiv:2101.03036 (2021) 52

2021 arXiv
[142]

IEEE Transactions on Instrumentation and Measurement72, 1–16 (2023)

Qi, B., Chen, Y., Liu, Q., He, X., Qing, L., Sheriff, R.E., Chen, H.: An image– text dual-channel union network for person re-identification. IEEE Transactions on Instrumentation and Measurement72, 1–16 (2023)

2023
[143]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Shao, Z., Zhang, X., Ding, C., Wang, J., Wang, J.: Unified pre-training with pseudo texts for text-to-image person re-identification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 11174–11184 (2023)

2023
[144]

arXiv preprint arXiv:2110.10807 (2021)

Han, X., He, S., Zhang, L., Xiang, T.: Text-based person search with limited data. arXiv preprint arXiv:2110.10807 (2021)

2021 arXiv
[145]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Jing, Y., Si, C., Wang, J., Wang, W., Wang, L., Tan, T.: Pose-guided multi- granularity attention network for text-based person search. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11189–11196 (2020)

2020
[146]

IEEE Transactions on Image Processing29, 5542–5556 (2020)

Niu, K., Huang, Y., Ouyang, W., Wang, L.: Improving description-based person re-identification by multi-granularity image-text alignments. IEEE Transactions on Image Processing29, 5542–5556 (2020)

2020
[147]

In: Proceed- ings of the 30th Acm International Conference on Multimedia, pp

Shao, Z., Zhang, X., Fang, M., Lin, Z., Wang, J., Ding, C.: Learning granularity- unified representations for text-to-image person re-identification. In: Proceed- ings of the 30th Acm International Conference on Multimedia, pp. 5566–5574 (2022)

2022
[148]

Journal of Visual Communication and Image Representation103, 104219 (2024)

Huang, B., Qi, X., Chen, B.: Cross-modal feature learning and alignment net- work for text–image person re-identification. Journal of Visual Communication and Image Representation103, 104219 (2024)

2024
[149]

Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Compu- tational Linguistics: Human Language Technolog...

2019
[150]

In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recogni- tion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

2016
[151]

arXiv preprint arXiv:2010.11929 (2020)

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

2010 arXiv
[152]

In: International Conference on Machine 53 Learning, pp

Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J.,et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine 53 Learning, pp. 8748–8763 (2021). PmLR

2021
[153]

arXiv preprint arXiv:2104.08860 (2021)

Luo, H., Ji, L., Zhong, M., Chen, Y., Lei, W., Duan, N., Li, T.: Clip4clip: An empirical study of clip for end to end video clip retrieval. arXiv preprint arXiv:2104.08860 (2021)

2021 arXiv
[154]

arXiv preprint arXiv:2106.11097 (2021)

Fang, H., Xiong, P., Xu, L., Chen, Y.: Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097 (2021)

2021 arXiv
[155]

In: Proceedings of the 45th International ACM SIGIR Con- ference on Research and Development in Information Retrieval, pp

Zhao, S., Zhu, L., Wang, X., Yang, Y.: Centerclip: Token clustering for efficient text-video retrieval. In: Proceedings of the 45th International ACM SIGIR Con- ference on Research and Development in Information Retrieval, pp. 970–981 (2022)

2022
[156]

In: European Conference on Computer Vision, pp

Ni, B., Peng, H., Chen, M., Zhang, S., Meng, G., Fu, J., Xiang, S., Ling, H.: Expanding language-image pretrained models for general video recognition. In: European Conference on Computer Vision, pp. 1–18 (2022). Springer

2022
[157]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Li, S., Sun, L., Li, Q.: Clip-reid: exploiting vision-language model for image re-identification without concrete text labels. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 1405–1413 (2023)

2023
[158]

IEEE Transactions on Image Processing32, 6032–6046 (2023)

Yan, S., Dong, N., Zhang, L., Tang, J.: Clip-driven fine-grained text-image per- son re-identification. IEEE Transactions on Image Processing32, 6032–6046 (2023)

2023
[159]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Cao, M., Bai, Y., Zeng, Z., Ye, M., Zhang, M.: An empirical study of clip for text-based person search. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 465–473 (2024)

2024
[160]

arXiv preprint arXiv:2401.02173 (2024)

Li, W., Tan, L., Dai, P., Zhang, Y.: Prompt decoupling for text-to-image person re-identification. arXiv preprint arXiv:2401.02173 (2024)

2024 arXiv
[161]

In: Proceedings of the 31st ACM International Conference on Multimedia, pp

Yan,S.,Dong,N.,Liu,J.,Zhang,L.,Tang,J.:Learningcomprehensiverepresen- tations with richer self for text-to-image person re-identification. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 6202–6211 (2023)

2023
[162]

Knowledge-Based Systems315, 113251 (2025)

Gou, Y., Li, Z., Zhang, J., Wang, Y., Ge, Y.: Instance-level feature bias cal- ibration learning for text-to-image person re-identification. Knowledge-Based Systems315, 113251 (2025)

2025
[163]

In: Proceedings of the 32nd ACM International Conference on Multimedia, pp

Yan, S., Liu, J., Dong, N., Zhang, L., Tang, J.: Prototypical prompting for text-to-image person re-identification. In: Proceedings of the 32nd ACM International Conference on Multimedia, pp. 2331–2340 (2024)

2024
[164]

In: 2023 IEEE International Conference on Image Processing 54 (ICIP), pp

Xu, W., Shao, Z., Ding, C.: Mining false positive examples for text-based person re-identification. In: 2023 IEEE International Conference on Image Processing 54 (ICIP), pp. 1680–1684 (2023). IEEE

2023
[165]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Qin, Y., Chen, Y., Peng, D., Peng, X., Zhou, J.T., Hu, P.: Noisy-correspondence learning for text-to-image person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 27197–27206 (2024)

2024
[166]

2786–2790 (2023)

Fujii, T., Tarashima, S.: Bilma: Bidirectional local-matching for text-based per- sonre-identification.In:ProceedingsoftheIEEE/CVFInternationalConference on Computer Vision, pp. 2786–2790 (2023)

2023
[167]

arXiv preprint arXiv:2304.09498 (2023)

Xiang, S., Gao, J., Guan, M., Ruan, J., Zhou, C., Liu, T., Qian, D., Fu, Y.: Learning robust visual-semantic embedding for generalizable person re- identification. arXiv preprint arXiv:2304.09498 (2023)

2023 arXiv
[168]

Expert Systems with Applications260, 125429 (2025)

Du, G., Gong, T., Zhang, L.: Graph-based consistent reconstruction and align- ment for imbalanced text–image person re-identification. Expert Systems with Applications260, 125429 (2025)

2025
[169]

In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp

Qin,Y.,Chen,C.,Fu,Z.,Peng,D.,Peng,X.,Hu,P.:Human-centeredinteractive learning via mllms for text-to-image person re-identification. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 14390–14399 (2025)

2025
[170]

Engineering Applications of Artificial Intelligence138, 109421 (2024)

Du, G., Zhu, H., Zhang, L.: Bottom-up color-independent alignment learning for text–image person re-identification. Engineering Applications of Artificial Intelligence138, 109421 (2024)

2024
[171]

In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp

Jiang, J., Ding, C., Tan, W., Wang, J., Tao, J., Xu, X.: Modeling thousands of human annotators for generalizable text-to-image person re-identification. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 9220–9230 (2025)

2025
[172]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Niu, K., Yu, H., Zhao, M., Fu, T., Yi, S., Lu, W., Li, B., Qian, X., Xue, X.: Cha- treid: Open-ended interactive person retrieval via hierarchical progressive tuning for vision language models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 24...

2025
[173]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Liu, Y., Liu, Z., Lan, X., Yang, W., Li, Y., Liao, Q.: Dm-adapter: Domain-aware mixture-of-adapters for text-based person retrieval. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 5703–5711 (2025)

2025
[174]

In: Proceedings of the 30th ACM International Conference on Multimedia, pp

Wang, Z., Zhu, A., Xue, J., Wan, X., Liu, C., Wang, T., Li, Y.: Look before you leap: Improving text-based person retrieval by learning a consistent cross-modal common manifold. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 1984–1992 (2022) 55

1984
[175]

IEEE Transactions on Multimedia25, 7699–7709 (2022)

Ji, Z., Hu, J., Liu, D., Wu, L.Y., Zhao, Y.: Asymmetric cross-scale alignment for text-based person search. IEEE Transactions on Multimedia25, 7699–7709 (2022)

2022
[176]

In: Proceedings ofthe30thACMInternationalConferenceonMultimedia,pp.4426–4434(2022)

Niu, K., Huang, L., Huang, Y., Wang, P., Wang, L., Zhang, Y.: Cross-modal co- occurrence attributes alignments for person search by language. In: Proceedings ofthe30thACMInternationalConferenceonMultimedia,pp.4426–4434(2022)

2022
[177]

IEEE Transactions on Multimedia26, 5065–5077 (2023)

Ma, W., Wu, X., Zhao, S., Zhou, T., Guo, D., Gu, L., Cai, Z., Wang, M.: Fedsh: Towards privacy-preserving text-based person re-identification. IEEE Transactions on Multimedia26, 5065–5077 (2023)

2023
[178]

In: Proceedings of the 31st ACM International Conference on Multimedia, pp

Shen, F., Shu, X., Du, X., Tang, J.: Pedestrian-specific bipartite-aware similar- ity learning for text-based person retrieval. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 8922–8931 (2023)

2023
[179]

In: Proceedings of the 31st ACM International Conference on Multimedia, pp

Ma, Y., Sun, X., Ji, J., Jiang, G., Zhuang, W., Ji, R.: Beat: Bi-directional one- to-many embedding alignment for text-based person retrieval. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 4157–4168 (2023)

2023
[180]

IEEE transactions on neural networks and learning systems (2023)

Yan, S., Tang, H., Zhang, L., Tang, J.: Image-specific information suppression and implicit local alignment for text-based person search. IEEE transactions on neural networks and learning systems (2023)

2023
[181]

IEEE transactions on image processing32, 3429–3441 (2023)

Niu, K., Huang, T., Huang, L., Wang, L., Zhang, Y.: Improving inconspicuous attributes modeling for person search by language. IEEE transactions on image processing32, 3429–3441 (2023)

2023
[182]

IEEE Transactions on Multimedia26, 4281–4293 (2023)

Bao, L., Wei, L., Zhou, W., Liu, L., Xie, L., Li, H., Tian, Q.: Multi-granularity matching transformer for text-based person search. IEEE Transactions on Multimedia26, 4281–4293 (2023)

2023
[183]

IEEE Trans- actions on Multimedia26, 6025–6036 (2023)

Han, G., Lin, M., Li, Z., Zhao, H., Kwong, S.: Text-to-image person re- identification based on multimodal graph convolutional network. IEEE Trans- actions on Multimedia26, 6025–6036 (2023)

2023
[184]

arXiv preprint arXiv:2303.04497 (2023)

Wang, G., Yu, F., Li, J., Jia, Q., Ding, S.: Exploiting the textual potential from vision-language pre-training for text-based person search. arXiv preprint arXiv:2303.04497 (2023)

2023 arXiv
[185]

IEEE Transactions on Image Processing33, 163–176 (2023)

He, S., Luo, H., Jiang, W., Jiang, X., Ding, H.: Vgsg: Vision-guided semantic- group network for text-based person search. IEEE Transactions on Image Processing33, 163–176 (2023)

2023
[186]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Jiang, D., Ye, M.: Cross-modal implicit relation reasoning and aligning for text- to-image person retrieval. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2787–2797 (2023) 56

2023
[187]

In: Proceedings of the 31st ACM International Conference on Multimedia, pp

Zang, X., Gao, W., Li, G., Fang, H., Ban, C., He, Z., Sun, H.: A baseline inves- tigation: transformer-based cross-view baseline for text-based person search. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 7737–7746 (2023)

2023
[188]

In: Proceedings of the 31st ACM International Conference on Multimedia, pp

Li, S., Xu, X., Yang, Y., Shen, F., Mo, Y., Li, Y., Shen, H.T.: Dcel: deep cross- modal evidential learning for text-based person retrieval. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 6292–6300 (2023)

2023
[189]

In: International Conference on Multimedia Modeling, pp

Gan, W., Liu, J., Zhu, Y., Wu, Y., Zhao, G., Zha, Z.-J.: Cross-modal semantic alignment learning for text-based person search. In: International Conference on Multimedia Modeling, pp. 201–215 (2024). Springer

2024
[190]

Image and Vision Computing142, 104912 (2024)

Xue, J., Wang, Z., Dong, G.-N., Zhu, A.: Eesso: Exploiting extreme and smooth signals via omni-frequency learning for text-based person retrieval. Image and Vision Computing142, 104912 (2024)

2024
[191]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Zuo, J., Zhou, H., Nie, Y., Zhang, F., Guo, T., Sang, N., Wang, Y., Gao, C.: Ufinebench: Towards text-based person retrieval with ultra-fine granularity. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22010–22019 (2024)

2024
[192]

International Journal of Computer Vision132(10), 4440–4455 (2024)

Sun, M., Suo, W., Wang, P., Niu, K., Liu, L., Lin, G., Zhang, Y., Wu, Q.: An adaptive correlation filtering method for text-based person search. International Journal of Computer Vision132(10), 4440–4455 (2024)

2024
[193]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Zhao, Z., Liu, B., Lu, Y., Chu, Q., Yu, N.: Unifying multi-modal uncertainty modeling and semantic alignment for text-to-image person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 7534– 7542 (2024)

2024
[194]

IEEE Transactions on Multimedia26, 10678–10691 (2024)

Li, J., Jiang, M., Kong, J., Tao, X., Luo, X.: Learning semantic polymorphic mapping for text-based person retrieval. IEEE Transactions on Multimedia26, 10678–10691 (2024)

2024
[195]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Liu, Y., Qin, G., Chen, H., Cheng, Z., Yang, X.: Causality-inspired invariant representation learning for text-based person retrieval. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 14052–14060 (2024)

2024
[196]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Yang, F., Li, W., Yang, M., Liang, B., Zhang, J.: Multi-modal disordered repre- sentation learning network for description-based person search. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 16316–16324 (2024)

2024
[197]

In: Proceedings of the 2024 International Conference on Multimedia Retrieval, 57 pp

Wang, D., Yan, F., Wang, Y., Zhao, L., Liang, X., Zhong, H., Zhang, R.: Fine- grained semantics-aware representation learning for text-based person retrieval. In: Proceedings of the 2024 International Conference on Multimedia Retrieval, 57 pp. 92–100 (2024)

2024
[198]

In: Proceedings of the 26th ACM International Conference on Multimedia, pp

Pang, L., Wang, Y., Song, Y.-Z., Huang, T., Tian, Y.: Cross-domain adversarial feature learning for sketch re-identification. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 609–617 (2018)

2018
[199]

In: Computer Vision & Pattern Recognition (2011)

Zhang, W., Wang, X., Tang, X.: Coupled information-theoretic encoding for face photo-sketch recognition. In: Computer Vision & Pattern Recognition (2011)

2011
[200]

In: Proceedings of the 30th ACM International Conference on Multimedia, pp

Zhang, Y., Wang, Y., Li, H., Li, S.: Cross-compatible embedding and semantic consistent feature construction for sketch re-identification. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 3347–3355 (2022)

2022
[201]

Neurocomputing403, 294–303 (2020)

Gui, S., Zhu, Y., Qin, X., Ling, X.: Learning multi-level domain invariant features for sketch re-identification. Neurocomputing403, 294–303 (2020)

2020
[202]

IEEE Transactions on Pattern Analysis and Machine Intelligence46(5), 2950–2964 (2023)

Chen, C., Ye, M., Qi, M., Du, B.: Sketchtrans: Disentangled prototype learning with transformer for sketch-photo recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence46(5), 2950–2964 (2023)

2023
[203]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Liu, X., Cheng, X., Chen, H., Yu, H., Zhao, G.: Differentiable auxiliary learning for sketch re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 3747–3755 (2024)

2024
[204]

In: Proceedings of the 31st ACM International Conference on Multimedia, pp

Lin, K., Wang, Z., Wang, Z., Zheng, Y., Satoh, S.: Beyond domain gap: exploit- ing subjectivity in sketch-based person retrieval. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 2078–2089 (2023)
[205]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Hu, Z., Yang, Z., Li, H., Wang, Z., Zou, Y.: Cross-category subjectivity gen- eralization for style-adaptive sketch re-id. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 22644–22653 (2025)

2025
[206]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Gong, Y., Hou, Y., Shi, J., Diep, K.L., Jiang, M.: A theory-inspired framework for few-shot cross-modal sketch person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, pp. 4284–4292 (2026)

2026
[207]

In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Yu, Q., Liu, F., Song, Y.-Z., Xiang, T., Hospedales, T.M., Loy, C.-C.: Sketch me that shoe. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 799–807 (2016)

2016
[208]

ACM Transactions on Graphics (TOG)35(4), 1–12 (2016)

Sangkloy, P., Burnell, N., Ham, C., Hays, J.: The sketchy database: learning to retrieve badly drawn bunnies. ACM Transactions on Graphics (TOG)35(4), 1–12 (2016)

2016
[209]

IEEE Transactions on Information Forensics and Security17, 3421–3432 (2022) 58

Zhu, F., Zhu, Y., Jiang, X., Ye, J.: Cross-domain attention and center loss for sketch re-identification. IEEE Transactions on Information Forensics and Security17, 3421–3432 (2022) 58

2022
[210]

In: International Forum on Digital TV and Wireless Multimedia Communications, pp

Chen, Q., Quan, Z., Zhao, K., Zheng, Y., Liu, Z., Li, Y.: A cross-modality sketch person re-identification model based on cross-spectrum image generation. In: International Forum on Digital TV and Wireless Multimedia Communications, pp. 312–324 (2021). Springer

2021
[211]

In: Proceedings of the 30th ACM International Conference on Multimedia, pp

Chen, C., Ye, M., Qi, M., Du, B.: Sketch transformer: Asymmetrical disentan- glement learning from dynamic synthesis. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 4012–4020 (2022)

2022
[212]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Guo, W., Pan, Z., Liang, Y., Xi, Z., Zhong, Z., Feng, J., Zhou, J.: Lidar- based person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17437–17447 (2024)

2024
[213]

In: Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems, pp

Liu,R.,Yao,T.,Shi,R.,Mei,L.,Wang,S.,Yin,Z.,Jiang,W.,Wang,S.:Mission: mmwave radar person identification with rgb cameras. In: Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems, pp. 309–321 (2024)

2024
[214]

Scientific Data 12(1), 1522 (2025)

Cascio, M., Cinque, L., Distante, D., Foresti, G.L., Fagioli, A.: A benchmark dataset for radio signal image-based person re-identification. Scientific Data 12(1), 1522 (2025)

2025
[215]

IEEE Transactions on Networking34, 2047–2060 (2025)

Sheng, B., Ding, P., Cai, H., Wang, C., Xiao, F., Qiu, T.: Toward adaptive person re-identification via mmwave radar point clouds. IEEE Transactions on Networking34, 2047–2060 (2025)
[216]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Fan, L., Li, T., Fang, R., Hristov, R., Yuan, Y., Katabi, D.: Learning longterm representations for person re-identification using radio signals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10699–10709 (2020)

2020
[217]

IEEE Internet of Things Journal (2025)

Han, C., Chen, S., Sheng, B., Guo, J., Sun, L.: mmreid: Person re-identification based on commodity millimeter-wave radar. IEEE Internet of Things Journal (2025)

2025
[218]

IEEE Internet of Things Journal (2025)

Wang, Y., Xu, M., Wang, Z., Wang, Y., Zhang, J.A.: User re-identification through mmwave radio imaging. IEEE Internet of Things Journal (2025)

2025
[219]

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies8(4), 1–27 (2024)

Li, K., Agarwal, D., Zhang, R., Gunda, V., Mo, T., Mahmud, S., Chen, B., Guimbretiěre, F., Zhang, C.: Sonicid: User identification on smart glasses with acoustic sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies8(4), 1–27 (2024)

2024
[220]

Knowledge-Based Systems264, 110331 (2023) 59

Wu, S., Zhai, X., Hu, Z., Sun, Y., Liu, J.: Advanced acoustic footstep-based person identification dataset and method using multimodal feature fusion. Knowledge-Based Systems264, 110331 (2023) 59

2023
[221]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Zheng,A.,Wang,Z.,Chen,Z.,Li,C.,Tang,J.:Robustmulti-modalitypersonre- identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 3529–3537 (2021)

2021
[222]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Wang, Y., Liu, X., Zhang, P., Lu, H., Tu, Z., Lu, H.: Top-reid: Multi-spectral object re-identification with token permutation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 5758–5766 (2024)

2024
[223]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Wang, Y., Liu, Y., Zheng, A., Zhang, P.: Decoupled feature-based mixture of experts for multi-modal object re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, pp. 8141–8149 (2025)

2025
[224]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Li, H., Li, C., Zhu, X., Zheng, A., Luo, B.: Multi-spectral vehicle re- identification: A challenge. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11345–11353 (2020)

2020
[225]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Wang, Z., Li, C., Zheng, A., He, R., Tang, J.: Interact, embed, and enlarge: Boosting modality-specific representations for multi-modal person re- identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2633–2641 (2022)

2022
[226]

arXiv preprint arXiv:2305.15762 (2023)

Zheng, A., He, Z., Wang, Z., Li, C., Tang, J.: Dynamic enhancement network for partial multi-modality person re-identification. arXiv preprint arXiv:2305.15762 (2023)

2023 arXiv
[227]

arXiv preprint arXiv:2310.18812 (2023)

Crawford, J., Yin, H., McDermott, L., Cummings, D.: Unicat: Crafting a stronger fusion baseline for multimodal re-identification. arXiv preprint arXiv:2310.18812 (2023)

2023 arXiv
[228]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Wang, Z., Huang, H., Zheng, A., He, R.: Heterogeneous test-time training for multi-modal person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, pp. 5850–5858 (2024)

2024
[229]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Zhang, P., Wang, Y., Liu, Y., Tu, Z., Lu, H.: Magic tokens: Select diverse tokens for multi-modal object re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 17117–17126 (2024)

2024
[230]

IEEE Transactions on Circuits and Systems for Video Technology (2024)

Yu, Z., Huang, Z., Hou, M., Pei, J., Yan, Y., Liu, Y., Sun, D.: Represen- tation selective coupling via token sparsification for multi-spectral object re- identification. IEEE Transactions on Circuits and Systems for Video Technology (2024)

2024
[231]

Expert Systems with Applications263, 125716 (2025) 60

Wu,D.,Liu, Z.,Chen, Z.,Gan,S., Tan,K.,Wan,Q., Wang,Y.:Lrmm: Lowrank multi-scale multi-modal fusion for person re-identification based on rgb-ni-ti. Expert Systems with Applications263, 125716 (2025) 60

2025
[232]

In: Proceedings of the 2022 International Conference on Multimedia Retrieval, pp

Zhai, Y., Zeng, Y., Cao, D., Lu, S.: Trireid: Towards multi-modal person re-identification via descriptive fusion model. In: Proceedings of the 2022 International Conference on Multimedia Retrieval, pp. 63–71 (2022)

2022
[233]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Chen, C., Ye, M., Jiang, D.: Towards modality-agnostic person re-identification with descriptive query. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15128–15137 (2023)

2023
[234]

arXiv preprint arXiv:2506.09385 (2025)

Zuo, J., Deng, Y., Tan, M., Jin, R., Wu, D.e.a.: Reid5o: Achieving omni multi-modal person re-identification in a single model. arXiv preprint arXiv:2506.09385 (2025)

2025
[235]

https://www

Doubao: Text-to-image Model, General Intelligent Drawing. https://www. volcengine.com/docs/6791/1354010 Accessed 2025-09-22

2025
[236]

https://ai.meitu.com/ Accessed 2025-09-22

Meitu: Structured Sketch. https://ai.meitu.com/ Accessed 2025-09-22

2025
[237]

In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp

Ha, R., Jiang, S., Li, B., Pan, B., Zhu, Y., Zhang, J., Zhu, X., Gong, S., Wang, J.: Multi-modal multi-platform person re-identification: Benchmark and method. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10251–10261 (2025)

2025
[239]

IEEE Transactions on Neural Networks and Learning Systems (2023)

Zhu, K., Guo, H., Zhang, S., al, W.: Aaformer: Auto-aligned transformer for person re-identification. IEEE Transactions on Neural Networks and Learning Systems (2023)

2023
[240]

arXiv preprint arXiv:2408.16684 (2024)

Tan, L., Dai, P., Chen, J., Cao, L., Wu, Y., Ji, R.: Partformer: Awakening latent diverse representation from vision transformer for object re-identification. arXiv preprint arXiv:2408.16684 (2024)

2024 arXiv
[241]

In: Proceedings of the 30th ACM International Conference on Multimedia, pp

Li, H., Ye, M., Wang, C., Du, B.: Pyramidal transformer with conv-patchify for person re-identification. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 7317–7326 (2022)

2022
[242]

International Journal of Computer Vision 133(5), 2410–2440 (2025) 61

Ye, M., Chen, S., Li, C., Zheng, W.-S., Crandall, D., Du, B.: Transformer for object re-identification: A survey. International Journal of Computer Vision 133(5), 2410–2440 (2025) 61

2025

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.

[1] [54]

In: Winter Conference on Applications of Computer Vision (WACV), pp

Kim, S., Gwon, S., Seo, K.: Enhancing diverse intra-identity representation for visible-infrared person re-identification. In: Winter Conference on Applications of Computer Vision (WACV), pp. 2513–2522 (2024)

2024

[2] [55]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Kim, M., Kim, S., Park, J., Park, S., Sohn, K.: Partmix: Regularization strat- egy to learn part discovery for visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18621–18632 (2023)

2023

[3] [99]

In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp

Yang, B., Chen, J., Ye, M.: Shallow-deep collaborative learning for unsuper- vised visible-infrared person re-identification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16870–16879 (2024)

2024

[4] [238]

In: Proceedings of the AAAI Conference on Artificial Intelligence, vol

Li, W., Zou, C., Wang, M., Xu, F., Zhao, J., Zheng, R., Cheng, Y., Chu, W.: Dc-former: Diverse and compact transformer for person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 1415– 1423 (2023)

2023

[5] [1]

Cao, C., Fu, X., Xu, S., Ge, C., Wang, K., Zha, Z.: Learning robust event- guided representations for person re-identification. Int. J. Comput. Vis.134(2), 82 (2026)

2026

[6] [2]

Dai, Y., Sun, Y., Liu, J., Tong, Z., Duan, L.: Bridging the source-to-target gap for cross-domain person re-identification with intermediate domains. Int. J. Comput. Vis.133(1), 410–434 (2025)

2025

[7] [3]

Zhang, G., Tan, S., Ji, Z., Fang, Y.: Dynamic attention vision-language trans- former network for person re-identification. Int. J. Comput. Vis.133(4), 1927–1939 (2025)

1927

[8] [4]

arXiv preprint arXiv:1610.02984 (2016)

Zheng, L., Yang, Y., Hauptmann, A.G.: Person re-identification: Past, present and future. arXiv preprint arXiv:1610.02984 (2016)

arXiv 2016