Pith. sign in

REVIEW 3 major objections 4 minor 2 cited by

Person re-identification can be unified into a single 'anytime' task spanning day/night and short/long time gaps, backed by a new 403k-image dataset and a multi-scenario model.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

AT-USTC, a 403k-image RGB/IR dataset covering six time-based ReID scenarios, and Uni-AT, a multi-scenario model, are proposed, with Uni-AT achieving 55.8% any-time Rank-1 on the new benchmark.

T0 review reviewed 2026-08-04 challenge →

load-bearing objection AT-USTC is a genuinely useful new ReID benchmark, but the evaluation protocol in Section 3 is written in a way that undercuts its own scenarios and needs a concrete fix before the numbers can be trusted. the 3 major comments →

arxiv 2509.16635 v2 pith:42Q6GPIU submitted 2025-09-20 cs.CV

Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification

Xulin Li , Yan Lu , Bin Liu , Jiaze Li , Qinhong Yang , Tao Gong , Qi Chu , Mang Ye
show 1 more author
Nenghai Yu
This is my paper
classification cs.CV
keywords person re-identificationanytime retrievalcross-modality ReIDcloth-changing ReIDmulti-scenario learningmixture-of-expertsbenchmark datasetsurveillance
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 introduces a new person re-identification task, Anytime Re-ID (AT-ReID): retrieving the same person whether the query and gallery images were captured during the day or night and whether the interval between captures is short or long enough for a clothing change. To support it, the authors built AT-USTC, a 403k-image dataset of 270 volunteers photographed repeatedly across 21 months with both RGB and infrared cameras, giving each identity many outfits, cameras, and sessions. They also propose Uni-AT, a single model that learns separate feature spaces for the six time-based scenarios and shares attribute-level experts among related ones. On AT-USTC, Uni-AT reaches 55.80 Rank-1 and 41.38 mAP across the six scenarios, and it transfers to existing ReID datasets better than task-specific methods. The point is that one model can replace separate daytime, nighttime, short-term, and long-term systems.

Core claim

The paper's central claim is that the six time-based retrieval scenarios—daytime short-term, daytime long-term, all-day short-term, all-day long-term, nighttime short-term, nighttime long-term—can be unified into a single task, and that a single model can outperform methods specialized for one scenario on all six. The evidence is AT-USTC, the first dataset that contains both RGB and infrared imagery and clothing changes per identity, and Uni-AT, which uses six scenario-specific classification tokens, a Mixture-of-Attribute-Experts module that shares time-moment and time-interval layers across related scenarios, and a hierarchical dynamic weighting loss. The authors report that Uni-AT achieve

What carries the argument

Three components carry the method. Multi-Scenario ReID (MS-ReID) attaches six CLS tokens to a Vision Transformer, one per scenario, each supervised by a scenario-aware identity loss that treats clothing IDs as categories in short-term scenarios and person IDs in long-term scenarios. Mixture-of-Attribute-Experts (MoAE) routes each scenario token through a sparse expert formed by combining a time-moment attribute layer (DT, AD, or NT) with a time-interval attribute layer (ST or LT), letting related scenarios share parameters. Hierarchical Dynamic Weighting (HDW) scales each scenario loss by the average confidence of scenarios sharing the same time-moment or time-interval attribute, balancing t

Load-bearing premise

The benchmark's validity rests on the unstated assumption that the six-scenario test protocol actually contains the intended variations—especially that long-term query-gallery pairs involve clothing changes and that this holds across modalities; the paper's one-line description of query/gallery selection says images come from the same camera and same clothing, which, taken literally, would remove the very variation the long-term scenarios are meant to test.

What would settle it

An independent audit of AT-USTC's test splits: count, for each identity in the DT-LT and NT-LT galleries, how many distinct outfits appear across their gallery images. If every identity's long-term gallery contains only one outfit per camera, then the reported long-term numbers cannot measure cloth-changing retrieval, and the Any-Time average would be inflated by a protocol mismatch.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • AT-USTC can serve as a common testbed for any model that claims to work across day/night and short/long-term retrieval, since it is the first dataset covering all six scenarios.
  • A single Uni-AT-style model can replace separate traditional, cross-modality, and cloth-changing ReID systems, because it outperforms task-specific baselines on its own scenarios as well as the others.
  • The dataset's high intra-identity diversity (29.1 captures per person) improves cross-dataset generalization, suggesting that ReID datasets should be judged not only by number of identities but by per-identity variation.
  • The scenario-aware identity loss—clothing categories for short-term, person IDs for long-term—lets the model exploit clothing when it is reliable and ignore it when it is not.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the protocol is audited and confirmed to include clothing changes within long-term galleries, AT-USTC could become the standard benchmark for evaluating 'anytime' capability; if not, the long-term numbers would need reinterpretation.
  • The attribute-layer sharing idea could transfer to other multi-scenario recognition problems, such as recognizing people across camera viewpoints or domains, where scenarios share partial attributes.
  • Because the dataset spans 21 months, it may also enable studies of how appearance changes over time, e.g., predicting a person's current look from past captures, which the paper does not explore.
  • One testable extension: train Uni-AT on AT-USTC and evaluate on a held-out subset of another cloth-changing dataset to see whether the any-time gains persist when the IR modality is absent.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This paper proposes Anytime Person Re-identification (AT-ReID), a multi-scenario re-identification task spanning daytime/nighttime and short-term/long-term retrieval, and introduces AT-USTC, a 403,599-image dataset of 270 volunteers captured by 16 RGB/IR cameras over 21 months with multiple clothing per identity. The authors also propose Uni-AT, a unified model combining a Multi-Scenario ReID transformer with six scenario-specific CLS tokens, a Mixture-of-Attribute-Experts module, and Hierarchical Dynamic Weighting. Experiments on AT-USTC report Any-Time Rank-1/mAP of 55.80/41.38, outperforming task-specific baselines and existing MoE variants; cross-dataset experiments on Market1501, CUHK03, SYSU-MM01, PRCC, and LTCC are also reported.

Significance. If the evaluation protocol is valid, this is a substantial benchmark contribution: AT-USTC is the first large-scale dataset with RGB and IR imagery, multiple clothing changes, and long-term multi-season collection covering all six time-based scenarios. The dataset scale, privacy safeguards, and code/data release are concrete strengths, as are the cross-dataset generalization experiments and component-wise ablations. However, the central claim rests on whether the six scenarios are actually constructed as defined, and the current description is ambiguous enough to require major revision before the benchmark numbers can be interpreted.

major comments (3)
  1. [Section 3 (Data Split)] The evaluation protocol is under-specified. The sentence "For each identity, we selected three query images and three gallery images from video clips featuring the same identity, captured by the same camera, and with the same clothing" would, taken literally, remove the cross-camera, cross-modality, and clothing-change variations that define DT-LT, NT-ST, NT-LT, AD-ST, and AD-LT. It also conflicts with the next sentence reporting an average gallery size of about 25 images per identity after selecting only three gallery images per identity. Table 3 suggests the LT scenarios are harder (DT-LT Rank-1 32.99 vs DT-ST 95.02), so the actual protocol likely differs from the text. Please specify, for each of the six scenarios, query and gallery modalities, camera overlap, clothing overlap, and image counts per identity; if the quoted sentence is a simplification, say so explicitly and describe th
  2. [Section 5 (Tables 3-5)] The performance differences supporting the method are often small: MoAE adds 1.67 Rank-1 over the Ls_id baseline, HDW adds 1.29, and the best Uni-AT is only 2.28 Rank-1 ahead of the strongest CM-ReID baseline (DEEN) in Table 5. No error bars, multiple seeds, or statistical tests are reported. Since the central claim is that Uni-AT outperforms task-specific methods across all scenarios, single-run results are not sufficient. Please report mean and standard deviation over at least three runs, or otherwise justify that the reported gaps are larger than run-to-run variation.
  3. [Section 5.4 / Table 6] The cross-dataset evaluation is not fully defined. Existing datasets cover only subsets of the six AT-ReID scenarios (e.g., Market1501 is essentially daytime short-term, PRCC/LTCC are daytime long-term, SYSU-MM01 is all-day short-term), yet Table 6 reports a single average R1/mAP and the text refers to cross-dataset Any-Time performance. Please describe how each dataset was mapped to scenarios, which CLS token or feature was used for each test set, and what the average is over. Without this, the generalization claim cannot be interpreted.
minor comments (4)
  1. [Section 5.3] The sentence listing cross-dataset improvements has a typo: "10.71% / 8.64% / 11.58% and 4.72% / 6.13%" does not match the five datasets or the values in Table 4 (the SYSU-MM01 improvement appears to be 11.48, not 11.58).
  2. [Section 3] Please reconcile "three gallery images per identity" with "the gallery contains an average of approximately 25 images per identity"; if the three images are per scenario, state that explicitly.
  3. [Section 1] Minor language issue: "This puts more challenges on the ReID system" should be "This poses more challenges to the ReID system".
  4. [Figure 4] Panel (c) and (d) would benefit from axis labels and units. The text refers to "Capture Times/Videos Per Person" and "Scale", but the figures do not make clear what the plotted values are.

Circularity Check

0 steps flagged

No significant circularity: Uni-AT is validated on a new benchmark and external datasets; Section 3 protocol ambiguity is a validity concern, not circularity.

full rationale

The derivation chain is self-contained. The paper's central claims are (1) AT-USTC is the first large-scale benchmark covering six time-based ReID scenarios, and (2) Uni-AT performs well on them and generalizes across datasets. Neither claim reduces to its inputs: the benchmark is new data collected over 21 months, and Uni-AT is compared against task-specific baselines and ablated on AT-USTC, with cross-dataset transfer to Market1501, CUHK03, SYSU-MM01, PRCC, and LTCC. The HDW weights are computed from the model's own training confidences, but they are an optimization weighting scheme, not a fitted parameter that is later reported as a prediction. The cited prior works include two of the authors' own papers (CIFT [22], CCIL [23]) used only as comparison methods, and [29] used to motivate scenario-specific features; none is load-bearing for the central claim. The only flagged issue is a protocol ambiguity in Section 3: 'For each identity, we selected three query images and three gallery images from video clips featuring the same identity, captured by the same camera, and with the same clothing.' If literal, it would conflict with the clothing-change definition of LT scenarios. However, the experimental results (DT-LT 32.99 Rank-1 vs DT-ST 95.02) indicate the LT split is in fact harder, so the sentence is likely an oversimplified description. This is a benchmark-validity/correctness risk, not a circular derivation, and does not change the circularity score.

Axiom & Free-Parameter Ledger

3 free parameters · 3 axioms · 0 invented entities

The central claims rest on the dataset's annotation quality and on a few domain assumptions about scenario-specific features and timestamp availability. The main free parameters are standard hyperparameters; none are fitted to the test set. No invented entities are introduced.

free parameters (3)
  • HDW exponent gamma = 0.5
    Fixed at 0.5 in Eq. 8, motivated by Focal Loss; no ablation or search shown, so the choice is a hand-set hyperparameter.
  • Number of experts (total) = 12 (6 scenarios x 2) in final model
    Table 2 compares 6 and 12 total experts; 12 gives best Any-Time performance, but the selection is empirical and no principled criterion is given.
  • Top-k in MoAE gating = 1
    Set to 1 following [1,31,41]; no ablation on k.
axioms (3)
  • domain assumption Scenario-specific features (color for daytime, clothing for short-term) lead to better retrieval than a single shared representation.
    Stated in Section 4.1 as the main principle of MS-ReID; if false, the multi-token design loses its justification.
  • domain assumption Timestamps of query and gallery are easily accessible in practical surveillance, making the scenario determinate.
    Section 4.1: 'shooting timestamps can be easily accessed'. If timestamps are unavailable or unreliable, AT-ReID reduces to the AD-LT default and the six-token design is less useful.
  • domain assumption The AT-USTC identity and clothing annotations are consistent across 21 months and 16 cameras.
    The entire benchmark evaluation depends on annotation quality; the paper describes the annotation effort but provides no inter-annotator agreement or manual verification statistics.

reviewed 2026-08-04 · how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification." pith.science (2026). https://pith.science/paper/42Q6GPIU

@misc{pith2026250916635,
  author       = {Pith},
  title        = {Pith review of: Towards Anytime Retrieval: A Benchmark for Anytime Person Re-Identification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/42Q6GPIU}},
  note         = {Machine review of arXiv:2509.16635}
}
Share X Bluesky LinkedIn Reddit HN
read the original abstract

In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, ranging from short-term to long-term. However, existing ReID tasks and datasets can not meet this requirement, as they are constrained by available time and only provide training and evaluation for specific scenarios. Therefore, we investigate a new task called Anytime Person Re-identification (AT-ReID), which aims to achieve effective retrieval in multiple scenarios based on variations in time. To address the AT-ReID problem, we collect the first large-scale dataset, AT-USTC, which contains 403k images of individuals wearing multiple clothes captured by RGB and IR cameras. Our data collection spans 21 months, and 270 volunteers were photographed on average 29.1 times across different dates or scenes, 4-15 times more than current datasets, providing conditions for follow-up investigations in AT-ReID. Further, to tackle the new challenge of multi-scenario retrieval, we propose a unified model named Uni-AT, which comprises a multi-scenario ReID (MS-ReID) framework for scenario-specific features learning, a Mixture-of-Attribute-Experts (MoAE) module to alleviate inter-scenario interference, and a Hierarchical Dynamic Weighting (HDW) strategy to ensure balanced training across all scenarios. Extensive experiments show that our model leads to satisfactory results and exhibits excellent generalization to all scenarios.

Figures

Figures reproduced from arXiv: 2509.16635 by Bin Liu, Jiaze Li, Mang Ye, Nenghai Yu, Qi Chu, Qinhong Yang, Tao Gong, Xulin Li, Yan Lu.

Figure 1
Figure 1. Figure 1: (a) AT-ReID aims to perform retrieval at any time, in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Six non-overlapping scenarios based on variations in [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The plan of the camera layout for collecting data. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Statistics of our AT-USTC and several popular ReID datasets. [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Each person in AT-USTC has been photographed 29.1 [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The pipeline of our Uni-AT. The DT, NT, AD, ST, and LT denote daytime, nighttime, all-day, short-term, and long-term cases, [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: t-SNE visualization of the feature distributions in six AT-ReID scenarios on the AT-USTC dataset. Dots of the same color [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Visualization of the retrieval ranking lists in six AT-ReID scenarios on the AT-USTC dataset. The green boxes represent correct [PITH_FULL_IMAGE:figures/full_fig_p012_8.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MMGait: Towards Multi-Modal Gait Recognition

    cs.CV 2026-04 conditional novelty 8.0

    MMGait provides a new multi-sensor gait dataset and OmniGait baseline to support single-modal, cross-modal, and unified multi-modal person identification from walking patterns.

  2. Beyond Visual Cues: Semantic-Driven Token Filtering and Expert Routing for Anytime Person ReID

    cs.CV 2026-04 unverdicted novelty 7.0

    STFER uses LVLM-generated identity-consistent semantic text to drive visual token filtering and expert routing for improved any-time person re-identification under clothing changes and modality shifts.

Reference graph

Works this paper leans on

61 extracted references · 8 linked inside Pith · cited by 2 Pith papers

  1. [1]

    Vlmo: Unified vision-language pre-training with mixture-of-modality-experts.Advances in Neural Information Processing Systems, 35:32897–32912,

    Hangbo Bao, Wenhui Wang, Li Dong, Qiang Liu, Owais Khan Mohammed, Kriti Aggarwal, Subhojit Som, Songhao Piao, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts.Advances in Neural Information Processing Systems, 35:32897–32912,

  2. [2]

    A notion of task relatedness yielding provable multiple-task learning guaran- tees.Machine learning, 73:273–287, 2008

    Shai Ben-David and Reba Schuller Borbely. A notion of task relatedness yielding provable multiple-task learning guaran- tees.Machine learning, 73:273–287, 2008. 3

    2008

  3. [3]

    Towards modality- agnostic person re-identification with descriptive query

    Cuiqun Chen, Mang Ye, and Ding Jiang. Towards modality- agnostic person re-identification with descriptive query. In Scenario Uni-AT (Ours) DT-ST : Daytime Short-term Scenario Uni-AT (Ours) Scenario Uni-AT (Ours) Scenario Uni-AT (Ours) Scenario Baseline Uni-AT (Ours) Scenario Uni-AT (Ours) DT-LT : Daytime Long-term NT-ST : Nighttime Short-term NT-LT : Nig...

    2023

  4. [4]

    Learning 3d shape feature for texture-insensitive person re-identification

    Jiaxing Chen, Xinyang Jiang, Fudong Wang, Jun Zhang, Feng Zheng, Xing Sun, and Wei-Shi Zheng. Learning 3d shape feature for texture-insensitive person re-identification. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8146–8155, 2021. 3

    2021

  5. [5]

    Unihcp: A unified model for human-centric perceptions

    Yuanzheng Ci, Yizhou Wang, Meilin Chen, Shixiang Tang, Lei Bai, Feng Zhu, Rui Zhao, Fengwei Yu, Donglian Qi, and Wanli Ouyang. Unihcp: A unified model for human-centric perceptions. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17840– 17852, 2023. 3

    2023

  6. [6]

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Syl- vain Gelly, et al. An image is worth 16x16 words: Trans- formers for image recognition at scale.arXiv preprint Method Query Rank-1 Rank-2 Rank-3 Rank-4 Rank-5 Rank-6 Rank-7 Baseline Uni-AT (Our...

    Pith/arXiv arXiv 2010

  7. [7]

    Visible-infrared per- son re-identification via semantic alignment and affinity in- ference

    Xingye Fang, Yang Yang, and Ying Fu. Visible-infrared per- son re-identification via semantic alignment and affinity in- ference. InProceedings of the IEEE/CVF International Con- ference on Computer Vision, pages 11270–11279, 2023. 3

    2023

  8. [8]

    Shape-erased feature learning for visible-infrared person re-identification

    Jiawei Feng, Ancong Wu, and Wei-Shi Zheng. Shape-erased feature learning for visible-infrared person re-identification. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22752–22761, 2023. 3

    2023

  9. [9]

    Clothes-changing person re-identification with rgb modality only

    Xinqian Gu, Hong Chang, Bingpeng Ma, Shutao Bai, Shiguang Shan, and Xilin Chen. Clothes-changing person re-identification with rgb modality only. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1060–1069, 2022. 3, 9

    2022

  10. [10]

    Dynamic task prioritization for multitask learning

    Michelle Guo, Albert Haque, De-An Huang, Serena Yeung, and Li Fei-Fei. Dynamic task prioritization for multitask learning. InProceedings of the European Conference on Computer Vision, pages 270–287, 2018. 3, 7

    2018

  11. [11]

    Semantic-aware consistency network for cloth- changing person re-identification

    Peini Guo, Hong Liu, Jianbing Wu, Guoquan Wang, and Tao Wang. Semantic-aware consistency network for cloth- changing person re-identification. InProceedings of the 31st ACM International Conference on Multimedia, pages 8730– 8739, 2023. 3

    2023

  12. [12]

    Clothing-change feature augmentation for person re-identification

    Ke Han, Shaogang Gong, Yan Huang, Liang Wang, and Tie- niu Tan. Clothing-change feature augmentation for person re-identification. InProceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 22066–22075, 2023. 3

    2023

  13. [13]

    Transreid: Transformer-based object re- identification

    Shuting He, Hao Luo, Pichao Wang, Fan Wang, Hao Li, and Wei Jiang. Transreid: Transformer-based object re- identification. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 15013–15022, 2021. 3, 9

    2021

  14. [14]

    Retrieve anyone: A general-purpose person re-identification task with instructions.arXiv preprint arXiv:2306.07520, 2023

    Weizhen He, Shixiang Tang, Yiheng Deng, Qihao Chen, Qingsong Xie, Yizhou Wang, Lei Bai, Feng Zhu, Rui Zhao, Wanli Ouyang, et al. Retrieve anyone: A general-purpose person re-identification task with instructions.arXiv preprint arXiv:2306.07520, 2023. 3

    Pith/arXiv arXiv 2023

  15. [15]

    Gaussian error linear units (gelus).arXiv preprint arXiv:1606.08415, 2016

    Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus).arXiv preprint arXiv:1606.08415, 2016. 6

    Pith/arXiv arXiv 2016

  16. [16]

    Adaptive mixtures of local experts.Neu- ral computation, 3(1):79–87, 1991

    Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. Adaptive mixtures of local experts.Neu- ral computation, 3(1):79–87, 1991. 3, 6

    1991

  17. [17]

    Cloth-changing person re- identification from a single image with gait prediction and regularization.arXiv preprint arXiv:2103.15537, 2021

    Xin Jin, Tianyu He, Kecheng Zheng, Zhiheng Yin, Xu Shen, Zhen Huang, Ruoyu Feng, Jianqiang Huang, Xian- Sheng Hua, and Zhibo Chen. Cloth-changing person re- identification from a single image with gait prediction and regularization.arXiv preprint arXiv:2103.15537, 2021. 3

    Pith/arXiv arXiv 2021

  18. [18]

    Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics

    Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geome- try and semantics. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7482– 7491, 2018. 3

    2018

  19. [19]

    All in one frame- work for multimodal re-identification in the wild

    He Li, Mang Ye, Ming Zhang, and Bo Du. All in one frame- work for multimodal re-identification in the wild. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17459–17469, 2024. 3

    2024

  20. [20]

    Clip-reid: exploiting vision-language model for image re-identification without concrete text labels

    Siyuan Li, Li Sun, and Qingli Li. Clip-reid: exploiting vision-language model for image re-identification without concrete text labels. InProceedings of the AAAI conference on artificial intelligence, pages 1405–1413, 2023. 9, 10

    2023

  21. [21]

    Deep- reid: Deep filter pairing neural network for person re- identification

    Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. Deep- reid: Deep filter pairing neural network for person re- identification. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 152–159,

  22. [22]

    Coun- terfactual intervention feature transfer for visible-infrared person re-identification

    Xulin Li, Yan Lu, Bin Liu, Yating Liu, Guojun Yin, Qi Chu, Jinyang Huang, Feng Zhu, Rui Zhao, and Nenghai Yu. Coun- terfactual intervention feature transfer for visible-infrared person re-identification. InProceedings of the European Conference on Computer Vision, pages 381–398. Springer,

  23. [23]

    Clothes-invariant feature learning by causal intervention for clothes-changing person re-identification.arXiv preprint arXiv:2305.06145, 2023

    Xulin Li, Yan Lu, Bin Liu, Yuenan Hou, Yating Liu, Qi Chu, Wanli Ouyang, and Nenghai Yu. Clothes-invariant feature learning by causal intervention for clothes-changing person re-identification.arXiv preprint arXiv:2305.06145, 2023. 3, 9

    Pith/arXiv arXiv 2023

  24. [24]

    Focal loss for dense object detection

    Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Doll´ar. Focal loss for dense object detection. InPro- ceedings of the IEEE International Conference on Computer Vision, pages 2980–2988, 2017. 7

    2017

  25. [25]

    Conflict-averse gradient descent for multi-task learn- ing.Advances in Neural Information Processing Systems, 34:18878–18890, 2021

    Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, and Qiang Liu. Conflict-averse gradient descent for multi-task learn- ing.Advances in Neural Information Processing Systems, 34:18878–18890, 2021. 3

    2021

  26. [26]

    Dual level adaptive weighting for cloth-changing person re-identification.IEEE Transactions on Image Processing, 2023

    Fangyi Liu, Mang Ye, and Bo Du. Dual level adaptive weighting for cloth-changing person re-identification.IEEE Transactions on Image Processing, 2023. 3

    2023

  27. [27]

    Towards impartial multi-task learning

    Liyang Liu, Yi Li, Zhanghui Kuang, J Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. Towards impartial multi-task learning. iclr, 2021. 3

    2021

  28. [28]

    End- to-end multi-task learning with attention

    Shikun Liu, Edward Johns, and Andrew J Davison. End- to-end multi-task learning with attention. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1871–1880, 2019. 3

    2019

  29. [29]

    Cross-modality person re-identification with shared-specific feature transfer

    Yan Lu, Yue Wu, Bin Liu, Tianzhu Zhang, Baopu Li, Qi Chu, and Nenghai Yu. Cross-modality person re-identification with shared-specific feature transfer. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13379–13389, 2020. 2, 3

    2020

  30. [30]

    Bag of tricks and a strong baseline for deep per- son re-identification

    Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, and Wei Jiang. Bag of tricks and a strong baseline for deep per- son re-identification. InProceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition Work- shops, pages 0–0, 2019. 3, 7, 9

    2019

  31. [31]

    Modeling task relationships in multi-task learning with multi-gate mixture-of-experts

    Jiaqi Ma, Zhe Zhao, Xinyang Yi, Jilin Chen, Lichan Hong, and Ed H Chi. Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. InProceed- ings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 1930–1939,

    1930

  32. [32]

    Snr: Sub-network routing for flexible parameter sharing in multi-task learning

    Jiaqi Ma, Zhe Zhao, Jilin Chen, Ang Li, Lichan Hong, and Ed H Chi. Snr: Sub-network routing for flexible parameter sharing in multi-task learning. InProceedings of the AAAI Conference on Artificial Intelligence, pages 216–223, 2019. 3

    2019

  33. [33]

    Beyond shared hi- erarchies: Deep multitask learning through soft layer order- ing.arXiv preprint arXiv:1711.00108, 2017

    Elliot Meyerson and Risto Miikkulainen. Beyond shared hi- erarchies: Deep multitask learning through soft layer order- ing.arXiv preprint arXiv:1711.00108, 2017. 3, 6

    Pith/arXiv arXiv 2017

  34. [34]

    Person recognition system based on a combination of body images from visible light and thermal cameras.Sensors, 17(3):605, 2017

    Dat Tien Nguyen, Hyung Gil Hong, Ki Wan Kim, and Kang Ryoung Park. Person recognition system based on a combination of body images from visible light and thermal cameras.Sensors, 17(3):605, 2017. 3, 4

    2017

  35. [35]

    Long-term cloth-changing person re-identification

    Xuelin Qian, Wenxuan Wang, Li Zhang, Fangrui Zhu, Yan- wei Fu, Tao Xiang, Yu-Gang Jiang, and Xiangyang Xue. Long-term cloth-changing person re-identification. InPro- ceedings of the Asian Conference on Computer Vision, 2020. 2, 3, 4, 7

    2020

  36. [36]

    Deepspeed-moe: Advanc- ing mixture-of-experts inference and training to power next- generation ai scale

    Samyam Rajbhandari, Conglong Li, Zhewei Yao, Minjia Zhang, Reza Yazdani Aminabadi, Ammar Ahmad Awan, Jeff Rasley, and Yuxiong He. Deepspeed-moe: Advanc- ing mixture-of-experts inference and training to power next- generation ai scale. InInternational conference on machine learning, pages 18332–18346. PMLR, 2022. 3

    2022

  37. [37]

    Large-scale spatio- temporal person re-identification: Algorithms and bench- mark.IEEE Transactions on Circuits and Systems for Video Technology, 32(7):4390–4403, 2021

    Xiujun Shu, Xiao Wang, Xianghao Zang, Shiliang Zhang, Yuanqi Chen, Ge Li, and Qi Tian. Large-scale spatio- temporal person re-identification: Algorithms and bench- mark.IEEE Transactions on Circuits and Systems for Video Technology, 32(7):4390–4403, 2021. 4

    2021

  38. [38]

    Ex- ploring the camera bias of person re-identification.arXiv preprint arXiv:2502.10195, 2025

    Myungseo Song, Jin-Woo Park, and Jong-Seok Lee. Ex- ploring the camera bias of person re-identification.arXiv preprint arXiv:2502.10195, 2025. 4

    Pith/arXiv arXiv 2025

  39. [39]

    Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline)

    Yifan Sun, Liang Zheng, Yi Yang, Qi Tian, and Shengjin Wang. Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). InPro- ceedings of the European Conference on Computer Vision, pages 480–496, 2018. 3

    2018

  40. [40]

    Circle loss: A unified perspective of pair similarity optimization

    Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, and Yichen Wei. Circle loss: A unified perspective of pair similarity optimization. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6398–6407, 2020. 3

    2020

  41. [41]

    Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations

    Hongyan Tang, Junning Liu, Ming Zhao, and Xudong Gong. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations. In Proceedings of the 14th ACM Conference on Recommender Systems, pages 269–278, 2020. 3, 6, 7, 8

    2020

  42. [42]

    Humanbench: Towards general human- centric perception with projector assisted pretraining

    Shixiang Tang, Cheng Chen, Qingsong Xie, Meilin Chen, Yizhou Wang, Yuanzheng Ci, Lei Bai, Feng Zhu, Haiyang Yang, Li Yi, et al. Humanbench: Towards general human- centric perception with projector assisted pretraining. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21970–21982, 2023. 3

    2023

  43. [43]

    Visualizing data using t-sne.Journal of machine learning research, 9 (11), 2008

    Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne.Journal of machine learning research, 9 (11), 2008. 10

    2008

  44. [44]

    When person re-identification meets changing clothes

    Fangbin Wan, Yang Wu, Xuelin Qian, Yixiong Chen, and Yanwei Fu. When person re-identification meets changing clothes. InProceedings of the IEEE/CVF conference on com- puter vision and pattern recognition workshops, pages 830– 831, 2020. 4

    2020

  45. [45]

    Multitask prompt tuning enables parameter-efficient transfer learning.arXiv preprint arXiv:2303.02861, 2023

    Zhen Wang, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, Huan Sun, and Yoon Kim. Multitask prompt tuning enables parameter-efficient transfer learning.arXiv preprint arXiv:2303.02861, 2023. 3

    Pith/arXiv arXiv 2023

  46. [46]

    Person transfer gan to bridge domain gap for person re- identification

    Longhui Wei, Shiliang Zhang, Wen Gao, and Qi Tian. Person transfer gan to bridge domain gap for person re- identification. InProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 79–88,

  47. [47]

    Rgb-infrared cross-modality per- son re-identification

    Ancong Wu, Wei-Shi Zheng, Hong-Xing Yu, Shaogang Gong, and Jianhuang Lai. Rgb-infrared cross-modality per- son re-identification. InProceedings of the IEEE Interna- tional Conference on Computer Vision, pages 5380–5389,

  48. [48]

    Deepchange: A long-term per- son re-identification benchmark with clothes change

    Peng Xu and Xiatian Zhu. Deepchange: A long-term per- son re-identification benchmark with clothes change. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11196–11205, 2023. 3, 4, 7

    2023

  49. [49]

    Towards grand unified representation learning for unsupervised visible-infrared per- son re-identification

    Bin Yang, Jun Chen, and Mang Ye. Towards grand unified representation learning for unsupervised visible-infrared per- son re-identification. InProceedings of the IEEE/CVF In- ternational Conference on Computer Vision, pages 11069– 11079, 2023. 3

    2023

  50. [50]

    Person re- identification by contour sketch under moderate clothing change.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6):2029–2046, 2019

    Qize Yang, Ancong Wu, and Wei-Shi Zheng. Person re- identification by contour sketch under moderate clothing change.IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(6):2029–2046, 2019. 2, 3, 4, 7

    2029

  51. [51]

    Good is bad: Causality inspired cloth-debiasing for cloth-changing person re-identification

    Zhengwei Yang, Meng Lin, Xian Zhong, Yu Wu, and Zheng Wang. Good is bad: Causality inspired cloth-debiasing for cloth-changing person re-identification. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1472–1481, 2023. 3, 9

    2023

  52. [52]

    Crandall, Ling Shao, and Jiebo Luo

    Mang Ye, Jianbing Shen, David J. Crandall, Ling Shao, and Jiebo Luo. Dynamic dual-attentive aggregation learn- ing for visible-infrared person re-identification. InComputer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XVII 16, pages 229–247. Springer, 2020. 3

    2020

  53. [53]

    Channel augmented joint learning for visible-infrared recog- nition

    Mang Ye, Weijian Ruan, Bo Du, and Mike Zheng Shou. Channel augmented joint learning for visible-infrared recog- nition. InProceedings of the IEEE/CVF international con- ference on computer vision, pages 13567–13576, 2021. 9

    2021

  54. [54]

    Deep learning for person re- identification: A survey and outlook.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

    Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven CH Hoi. Deep learning for person re- identification: A survey and outlook.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 3

    2021

  55. [55]

    Entire-id: An exten- sive and diverse dataset for person re-identification

    Serdar Yıldız and Ahmet Nezih Kasım. Entire-id: An exten- sive and diverse dataset for person re-identification. In2024 IEEE 18th International Conference on Automatic Face and Gesture Recognition (FG), pages 1–5. IEEE, 2024. 4

    2024

  56. [56]

    Gradient surgery for multi-task learning.Advances in Neural Information Pro- cessing Systems, 33:5824–5836, 2020

    Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. Gradient surgery for multi-task learning.Advances in Neural Information Pro- cessing Systems, 33:5824–5836, 2020. 3

    2020

  57. [57]

    Diverse embedding expan- sion network and low-light cross-modality benchmark for visible-infrared person re-identification

    Yukang Zhang and Hanzi Wang. Diverse embedding expan- sion network and low-light cross-modality benchmark for visible-infrared person re-identification. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2153–2162, 2023. 3, 4, 7, 9

    2023

  58. [58]

    Scalable person re-identification: A benchmark

    Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jing- dong Wang, and Qi Tian. Scalable person re-identification: A benchmark. InProceedings of the IEEE International Conference on Computer Vision, pages 1116–1124, 2015. 1, 3, 4, 7

    2015

  59. [59]

    A versa- tile framework for multi-scene person re-identification.IEEE Transactions on Pattern Analysis and Machine Intelligence,

    Wei-Shi Zheng, Junkai Yan, and Yi-Xing Peng. A versa- tile framework for multi-scene person re-identification.IEEE Transactions on Pattern Analysis and Machine Intelligence,

  60. [60]

    Random erasing data augmentation

    Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang. Random erasing data augmentation. InProceed- ings of the AAAI conference on artificial intelligence, pages 13001–13008, 2020. 7

    2020

  61. [61]

    Uni- perceiver-moe: Learning sparse generalist models with con- ditional moes.Advances in Neural Information Processing Systems, 35:2664–2678, 2022

    Jinguo Zhu, Xizhou Zhu, Wenhai Wang, Xiaohua Wang, Hongsheng Li, Xiaogang Wang, and Jifeng Dai. Uni- perceiver-moe: Learning sparse generalist models with con- ditional moes.Advances in Neural Information Processing Systems, 35:2664–2678, 2022. 3

    2022

This paper was first reviewed by deepseek-v4-flash on August 4, 2026.