Pith. sign in

REVIEW 4 major objections 5 minor 64 references

Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

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

Pith's one-line read In a frozen ReID backbone, low- and high-resolution features differ by a consistent, identity-independent direction, and a small posthoc network that pans features along it beats super-resolution approaches.

desk verdict A genuine cross-resolution effect, supported by held-out results, but the paper needs cleanup on missing Duke results, error bars, and baselines before I'd trust the exact numbers. read the letter →

arxiv 2510.00936 v2 pith:O7YHTHL4 submitted 2025-10-01 cs.CV

Zanwu Liu , Chao Yuan , Bo Li , Xiaowei Zhang , Guanglin Niu This is my paper · ORCID
classification cs.CV
keywords cross-resolutionpersonre-identificationfeaturedirectionvectorpanningpseudo-HRfeaturespost-hocalignmentresolutionshiftembeddingspacelightweightMLP
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 claims that resolution, not just identity, is encoded as a direction in a person re-identification backbone's feature space: after averaging over identities, the difference between high- and low-resolution feature vectors points in a consistent direction that transfers across people and datasets. It validates this with cosine similarity of mean offsets from disjoint identity sets, canonical correlation analysis, and Pearson correlation analysis. Building on that, it proposes VPFA, a small post-hoc MLP that learns to 'pan' a low-resolution feature along this resolution direction to produce a pseudo-high-resolution feature, trained only on identity-mean feature pairs and using the backbone frozen. If correct, the approach gives a cheap, drop-in alignment layer that outperforms super-resolution-based and jointly trained cross-resolution ReID methods on several benchmarks, and also transfers across datasets and to other modality gaps. The sympathetic reader cares because it reframes cross-resolution matching as a linear-ish embedding-space correction rather than as an image-restoration problem.

What carries the argument

The central objects are (1) the 'resolution direction' — the average HR−LR feature difference in the frozen backbone, shown to be stable once identity information is averaged out, and (2) the Vector Panning module (VPFA), a lightweight gated residual MLP that estimates a correction vector f(z_LR) added to the original LR feature: z_hat = z_LR + tanh(W4·VP(z_LR)), trained with squared error between corrected and HR features. The residual-plus-tanh design keeps corrections bounded, preserves identity content, and makes the shift learnable from identity-mean feature pairs alone; the loss's expansion as r²+R²−2rRcosθ shows it explicitly aligns both direction and magnitude, matching the cosine me

What would settle it

Train the VP module on identity-mean HR/LR pairs from one backbone, then apply it to individual (non-identity-averaged) LR images and compare retrieval against the uncorrected baseline. If the per-image HR–LR difference vectors of a held-out identity are not aligned with the learned global direction (e.g., cosine similarity to the global shift near zero, or the correction reliably lowers Rank-1 for many individuals), the central claim fails. A direct look: compute cosine similarity between an individual identity's HR−LR offset and the global offset on a new dataset; if it is near zero or negat

Watch

Extended reading notes

Core claim

On the paper's own terms: standard ReID backbones trained on high-resolution images already contain a stable, linearizable semantic direction for resolution. The authors demonstrate that average HR−LR feature offsets computed from disjoint identity subsets have cosine similarity above 0.99 on Market-1501 and CUHK03, that canonical correlations between HR and LR feature matrices far exceed those of random matrices, and that per-identity Pearson correlations with the global shift are mostly above 0.5 and rise with resolution gap. On this basis, they define a resolution vector and construct VPFA to move LR features along it: a three-layer MLP with LayerNorm and a Tanh-gated residual, trained wi

Load-bearing premise

VPFA works only if the HR–LR discrepancy in a frozen backbone is a stable, identity-independent additive offset: a single MLP trained on identity-mean differences must transfer to individual images, and low-resolution features must retain enough identity information for a residual correction to restore HR-like matchability.

Editorial extensions

If this is right

  • If the resolution-direction finding holds, cross-resolution ReID can be addressed as a post-processing step: any existing backbone can be made cross-resolution-robust by learning a single small network, without super-resolution or joint training.
  • The reported numbers imply that a feature-level correction can outperform pixel-level restoration methods (e.g., +17.7 Rank-1 over a GAN-based SR method on MLR-Market-1501), suggesting that much of the resolution gap in ReID is a systematic embedding bias rather than lost image detail.
  • Because the VP module transfers across datasets (trained on one, tested on another), the learned direction appears to be a property of the embedding geometry rather than of a specific camera domain, so the module could be trained once and deployed broadly.
  • The same panning idea extends beyond resolution: the authors report improvements when applied to visible-infrared and text-image ReID, implying the mechanism may generalize to other identity-independent modality shifts.
  • The approach adds negligible inference cost (millions of samples per second per the paper) so it can be inserted into deployed ReID systems with minimal overhead.

Reading between the lines

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

  • A testable extension: if the shift is truly identity-independent and near-linear, a VP trained on labelled identities should also rescue unlabelled LR queries in the wild; measuring this gap is a natural next experiment the paper does not run.
  • One could quantify how far the linearity assumption breaks by comparing the VP correction against a per-identity oracle that tunes the shift; the difference would reveal how much of the resolution effect is content-dependent rather than fixed.
  • The learned resolution direction might serve as an interpretable axis: projecting unseen features onto it could yield a resolution-quality score or a detector for low-resolution inputs, which the paper leaves unexplored.
  • If a single global shift works for resolution, the same panning recipe might apply to other nuisance factors like pose, viewpoint, or illumination, provided those also produce stable identity-mean offsets.
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.

Referee Report

4 major / 5 minor

Summary. This paper studies cross-resolution person re-identification (CR-ReID) from a feature-space viewpoint. It reports an empirical observation: after averaging identity-specific variation, HR–LR feature difference vectors extracted from a frozen ReID backbone are highly consistent across identities (Table 1) and show high CCA/Pearson correlations (§3.2). On this basis it proposes VPFA, a post-hoc MLP-based module that adds a gated residual to LR features (Eq. 3) to approximate HR features, trained with an MSE loss on identity-level prototype pairs. Experiments on MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, and CA VIAR report state-of-the-art Rank-1 results, with ablations, cross-domain transfer, and cross-modality applications in the appendix.

Significance. The proposed recipe is simple, reproducible, and if valid would give a practical post-hoc plug-in for CR-ReID that does not require super-resolution or backbone fine-tuning. The held-out identity protocol in §4.4 means the headline evaluation is not circular; the public code and cross-dataset transfer experiment (Appendix A.7) are useful strengths. The core regression formulation is clean and easy to implement. However, the paper's evidence for the 'resolution direction' is weakened by a Pearson protocol that appears circular and an underspecified CCA baseline, and the narrative contains internal contradictions (missing DukeMTMC results, 'no ID labels' claim). These are fixable but require revision before the SOTA claims can be fully accepted.

major comments (4)
  1. [§5.1 and Table 3] Section 5.1 says the evaluation uses 'four synthetic benchmarks—Market-1501, CUHK08, VIPeR, and DukeMTMC—and one real-world dataset, CA VIAR', and §5.3 claims the best Rank-1 on all four datasets. However, Table 3 contains only MLR-Market-1501, MLR-CUHK03, MLR-VIPeR, and CA VIAR; DukeMTMC never appears. Either add the DukeMTMC results or revise the dataset list and the 'four benchmarks' claim. Please also reconcile 'CUHK08' with the 'MLR-CUHK03' used in the table.
  2. [§4.4 and Algorithm 1] The text states 'Our training requires no ID labels' and describes VPL as 'ID-agnostic', but Eq. (2) and Algorithm 1 construct (z̄_LR, z̄_HR) by grouping features by identity and computing identity-wise means. Identity labels are therefore required for training. This contradicts a claimed advantage and should be corrected—either drop the label-free claim or specify what weaker supervision (e.g., tracklets) suffices.
  3. [§3.2 and Table 2] The Pearson analysis appears to estimate one global HR–LR shift vector and then correlate it with mean difference vectors of 25 identity pairs drawn from the same 50 identities. If the global vector is the average of all 50 identities' differences, the paired means are part of that average and the correlations are inflated by construction. The estimation protocol must be stated explicitly; if this is the case, use a leave-one-pair-out or disjoint-subset estimate. Similarly, Table 2a does not define how 'random matrices' are generated, and high canonical correlation between HR and LR features of the same identities is expected even without a resolution-specific direction. Since this section is the statistical justification for the paper's central empirical claim, these analyses need to be rigorous.
  4. [Table 3 and Appendix A.5] Per-split results in Appendix A.5 show large variance (e.g., MLR-VIPeR Rank-1 ranges 34.8–57.9 across splits), yet Table 3 reports only averages. Without standard deviations or significance tests, the claimed SOTA margins are difficult to assess. Moreover, VPFA uses TransReID, a stronger backbone than several compared methods; a same-backbone comparison or a table showing the baseline backbone's results on each dataset would make the +3.8% gain more interpretable.
minor comments (5)
  1. [Algorithm 1] Step (c) says 'Compute loss L_VPL using Eq. (2)', but Eq. (2) defines identity-level features, not the loss (which is Eq. (6)/(8)). Update the cross-reference.
  2. [§1 and References] The citation 'Neel et al.' refers to an Alignment Forum blog post rather than a peer-reviewed publication; replace with a formal citation or remove it.
  3. [§5.4] The efficiency number '4,424,371.31 samples/second' is over-precise and lacks a measurement protocol or comparison baseline. State how it was measured and compare latency/FLOPs with at least one SR-based alternative. Also, 24.14M parameters is not obviously 'lightweight' relative to the backbone; justify.
  4. [Appendix A.3] No training details are given for the VI and TI applications of VPFA. How are paired features and prototype pairs formed for visible-infrared and text-image ReID? Without this, the generalization claim is not fully reproducible.
  5. [Appendix A.7] The cross-domain transfer results are reported without the backbone's baseline accuracy on the target dataset. Include TransReID's direct Rank-1 on MLR-CUHK03/MLR-Market to isolate the transfer effect.

Circularity Check

0 steps flagged · score 3.0 of 10

One self-referential in-sample Pearson validation; central VPFA evaluation and cross-domain results are not circular.

full rationale

The central derivation is not circular. VPFA is a post-hoc MLP trained on identity-mean HR/LR feature pairs (Eq. 2, Eq. 8) and evaluated on standard held-out identity splits (751/750 for Market-1501, 1367/100 for CUHK03, non-overlapping halves for VIPeR/CAVIAR), as well as in cross-domain transfer experiments (Table 11). The headline Rank-1 improvements are therefore genuine out-of-sample results rather than fitted predictions. The claimed resolution direction is also independently supported by Table 1, which uses disjoint ID subsets, and by CCA comparisons against random matrices. The only self-referential step found is the Pearson 'local consistency' analysis in Section 3.2: the global shift is computed from the same identity vectors (grouped into 25 pair means) that are then correlated with it, so the reported correlations are inflated and the stated null is incorrect. This is a localized statistical-validation circularity, not a fitted parameter renamed as a prediction, and it does not force the benchmark results. Self-citations in the reference list and Table 3 (Yuan et al. 2025 works by current authors) are used as baselines or context, not as load-bearing uniqueness or ansatz justifications.

Assumptions & free parameters 8 free parameters · 6 assumptions · 1 invented entities

The central result rests on a learned MLP regression (fitted parameters) and on domain assumptions about frozen backbone features and downsampling. There are no invented physical entities; the 'resolution direction' is a statistical construct with some independent cross-domain evidence.

free parameters (8)
  • VP MLP weights = ~24.14M parameters
    Trained end-to-end on paired identity-mean LR/HR features (Eq. 8); the entire correction function is fit.
  • Hidden dimension = 2048
    Chosen via ablation on MLR-Market1501 (Table 4b); 512 underfits, 3840 saturates.
  • Number of VP blocks = 3
    Ablation in Table 4b; 4 blocks degrades.
  • Initialization std = 1e-3
    Zero-mean Gaussian init so the initial residual is near zero (§4.2).
  • Learning rate / weight decay = lr=2e-4, wd=1e-5
    Adam settings in §5.2.
  • Training epochs / batch size = 120 / 32
    Settings in §5.2.
  • Training pair count = 5000
    Identity-level shuffled pairs sampled in §5.2.
  • Global resolution shift vector (analysis) = mean(HR-LR) per dataset
    Computed in §3.2 as the reference direction for Pearson correlations; estimated from the same data it validates.
assumptions (6)
  • domain assumption Pretrained TransReID features preserve identity-discriminative information under 2x-7x downsampling.
    VPFA can only shift features, not recover missing information; if downsampling destroys identity cues, post-hoc correction cannot restore them (used throughout §4-5).
  • domain assumption Identity-wise mean pooling yields stable HR/LR prototypes that represent the resolution shift.
    Eq. 2 averages all images per identity; assumes pose/occlusion noise cancels and the residual direction survives.
  • ad hoc to paper The HR-LR discrepancy is well-modeled as an additive residual: z_LR + Gate(MLP(z_LR)) ≈ z_HR.
    Eq. 3 posits a single learned transformation; this is the design assumption of the method, not derived from the statistical analysis.
  • ad hoc to paper The word-embedding vector-offset analogy transfers to vision embedding spaces.
    Section 3.1 analogizes Vec(King)-Vec(Man) to resolution offsets; this is motivation, not proven.
  • standard math CCA and Pearson correlation conventions (0.4-0.5 thresholds) indicate meaningful association.
    Used in §3.2 to interpret results (Johnson & Wichern; Cohen).
  • domain assumption Synthetic downsampling by factors 2-7 is an adequate proxy for real resolution degradation.
    Most benchmarks are synthetically downsampled; CA VIAR provides one real-world check, but VP training on CA VIAR uses its own pairs.
invented entities (1)
  • Resolution-specific semantic direction independent evidence
    purpose: Explains HR-LR feature discrepancy and motivates VPFA.
    Not a physical entity but a statistical regularity. Independent evidence comes from cross-dataset VP transfer (Table 11), real-world CA VIAR gains, and t-SNE gap reduction (Fig. 3).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification." pith.science (2026). https://pith.science/paper/O7YHTHL4

@misc{pith2026251000936,
  author       = {Pith},
  title        = {Pith review of: Resolution as a Direction: Vector-Panning Feature Alignment for Cross-Resolution Re-Identification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O7YHTHL4}},
  note         = {Machine review of arXiv:2510.00936}
}
read the original abstract

Cross-resolution person re-identification (CR-ReID) remains challenging in practical surveillance, where camera quality and capture distance lead to substantial resolution gaps between low-resolution (LR) queries and high-resolution (HR) gallery images. Prior approaches commonly rely on super-resolution (SR) or resolution-invariant representation learning, which often increases system complexity and may not directly address the feature mismatch induced by resolution degradation. In this work, we report a new empirical finding from a dedicated analysis in which identity-specific variation is averaged out: the HR--LR feature discrepancy produced by standard ReID backbones exhibits a consistent, resolution-related semantic direction in the embedding space. We further support this observation with statistical analyses based on Canonical Correlation Analysis (CCA) and Pearson correlation analysis. Motivated by this finding, we propose Vector Panning Feature Alignment (VPFA), a lightweight post-hoc module that learns to pan LR features along the learned resolution direction to obtain pseudo-HR representations. VPFA operates after feature extraction and can be integrated into existing ReID systems with negligible overhead. Extensive experiments on multiple CR-ReID benchmarks show that VPFA achieves state-of-the-art performance while improving efficiency compared to SR-based or jointly trained alternatives.

Figures

Figures reproduced from arXiv: 2510.00936 by the authors.

Figure 1
Figure 1. The motivation of our paper. Top: semantic offset in word embedding space. Mid: [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. An overview of the proposed Vector Panning based Feature Alignment (VPFA) framework. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. t-SNE visualization of features from 12 identities. Colors indicate different person IDs, [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Visualization of Retrieval Results Before and After Applying Vector Panning based Fea [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

64 extracted references · 6 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

  2. [2]

    Torr, and Longin Jan Latecki

    Song Bai, Peng Tang, Philip H.S. Torr, and Longin Jan Latecki. Re-ranking via metric fusion for object retrieval and person re-identification. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 740--749, 2019. doi:10.1109/CVPR.2019.00083

    arXiv 2019

  3. [3]

    Ice: Inter-instance contrastive encoding for unsupervised person re-identification

    Hao Chen, Benoit Lagadec, and François Br \'e mond. Ice: Inter-instance contrastive encoding for unsupervised person re-identification. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp.\ 14940--14949, 2021. URL https://api.semanticscholar.org/CorpusID:232417748

    2021

  4. [4]

    Person re-identification by camera correlation aware feature augmentation

    Ying-Cong Chen, Xiatian Zhu, Wei-Shi Zheng, and Jian-Huang Lai. Person re-identification by camera correlation aware feature augmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 0 (2): 0 392--408, 2018. doi:10.1109/TPAMI.2017.2666805

    arXiv 2018

  5. [5]

    Yun-Chun Chen, Yu-Jhe Li, Xiaofei Du, and Y. Wang. Learning resolution-invariant deep representations for person re-identification. In AAAI Conference on Artificial Intelligence, 2019. URL https://api.semanticscholar.org/CorpusID:57541847

    2019

  6. [6]

    Inter-task association critic for cross-resolution person re-identification

    Zhiyi Cheng, Qi Dong, Shaogang Gong, and Xiatian Zhu. Inter-task association critic for cross-resolution person re-identification. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 2602--2612, 2020. doi:10.1109/CVPR42600.2020.00268

    arXiv 2020

  7. [7]

    Statistical Power Analysis for the Behavioral Sciences

    Jacob Cohen. Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, revised edition edition, 1988. doi:10.4324/9780203771587. URL https://doi.org/10.4324/9780203771587

    doi:10.4324/9780203771587 1988

  8. [8]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv, abs/2010.11929, 2020. URL https://api.semanticscholar.org/CorpusI...

    arXiv 2010

Show all 64 references
  1. [9]

    Human co-parsing guided alignment for occluded person re-identification

    Shuguang Dou, Cairong Zhao, Xinyang Jiang, Shanshan Zhang, Wei-Shi Zheng, and Wangmeng Zuo. Human co-parsing guided alignment for occluded person re-identification. IEEE Transactions on Image Processing, 32: 0 458--470, 2023. doi:10.1109/TIP.2022.3229639

    2023

  2. [11]

    Fd-gan: Pose-guided feature distilling gan for robust person re-identification

    Yixiao Ge, Zhuowan Li, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, and Hongsheng Li. Fd-gan: Pose-guided feature distilling gan for robust person re-identification. In Neural Information Processing Systems, 2018. URL https://api.semanticscholar.org/CorpusID:52941531

    2018

  3. [12]

    Person Re-Identification

    Shaogang Gong, Marco Cristani, Shuicheng Yan, and Chen Change Loy. Person Re-Identification. Springer Publishing Company, Incorporated, 2014. ISBN 1447162951

    2014

  4. [13]

    Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C

    Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial nets. In Neural Information Processing Systems, 2014. URL https://api.semanticscholar.org/CorpusID:261560300

    2014

  5. [14]

    Viewpoint invariant pedestrian recognition with an ensemble of localized features

    Douglas Gray and Hai Tao. Viewpoint invariant pedestrian recognition with an ensemble of localized features. DBLP, 2008

    2008

  6. [15]

    Prediction and recovery for adaptive low-resolution person re-identification

    Ke Han, Yan Huang, Zerui Chen, Liang Wang, and Tieniu Tan. Prediction and recovery for adaptive low-resolution person re-identification. In Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVI, pp.\ 193–209, Berlin, Hei...

    2020 doi

  7. [16]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 770--778, 2016. doi:10.1109/CVPR.2016.90

    2016 doi

  8. [17]

    Wang, Hao Li, and Wei Jiang

    Shuting He, Haowen Luo, Pichao Wang, F. Wang, Hao Li, and Wei Jiang. Transreid: Transformer-based object re-identification. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp.\ 14993--15002, 2021. URL https://api.semanticscholar.org/CorpusID:231846818

    2021

  9. [18]

    Divide-and-regroup clustering for domain adaptive person re-identification

    Zhengdong Hu, Yifan Sun, Yi Yang, and Jianguang Zhou. Divide-and-regroup clustering for domain adaptive person re-identification. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligen...

    2022 doi

  10. [19]

    Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval

    Ding Jiang and Mang Ye. Cross-modal implicit relation reasoning and aligning for text-to-image person retrieval. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 2787--2797, 2023. URL https://api.semanticscholar.org/CorpusID:257663606

    2023

  11. [20]

    Deep low-resolution person re-identification

    Jiening Jiao, Wei-Shi Zheng, Ancong Wu, Xiatian Zhu, and Shaogang Gong. Deep low-resolution person re-identification. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and ...

    2018

  12. [21]

    Weakly supervised pedestrian segmentation for person re-identification

    Ziqi Jin, Jinheng Xie, Bizhu Wu, and Linlin Shen. Weakly supervised pedestrian segmentation for person re-identification. IEEE Transactions on Circuits and Systems for Video Technology, 33 0 (3): 0 1349--1362, 2023. doi:10.1109/TCSVT.2022.3210476

    2023

  13. [22]

    R. A. Johnson and D. W. Wichern. Applied multivariate statistical analysis. Prentice-Hall, Inc., USA, 1988. ISBN 0130411469

    1988

  14. [23]

    Photo-realistic single image super-resolution using a generative adversarial network

    Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi. Photo-realistic single image super-resolution using a generative adversarial network. In 2017 IEEE Confer...

    2017 doi

  15. [24]

    Camera-driven representation learning for unsupervised domain adaptive person re-identification

    Geon Lee, Sanghoon Lee, Dohyung Kim, Younghoon Shin, Yongsang Yoon, and Bumsub Ham. Camera-driven representation learning for unsupervised domain adaptive person re-identification. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp.\ 11419--11428, 2023. do...

    2023

  16. [25]

    Unsupervised tracklet person re-identification

    Minxian Li, Xiatian Zhu, and Shaogang Gong. Unsupervised tracklet person re-identification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42 0 (7): 0 1770--1782, 2020. doi:10.1109/TPAMI.2019.2903058

    2020

  17. [26]

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

    Wei Li, Rui Zhao, Tong Xiao, and Xiaogang Wang. Deepreid: Deep filter pairing neural network for person re-identification. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp.\ 152--159, 2014. doi:10.1109/CVPR.2014.27

    2014 doi

  18. [27]

    Harmonious attention network for person re-identification

    Wei Li, Xiatian Zhu, and Shaogang Gong. Harmonious attention network for person re-identification. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 2285--2294, 2018. URL https://api.semanticscholar.org/CorpusID:3458516

    2018

  19. [28]

    Multi-scale learning for low-resolution person re-identification

    Xiang Li, Wei-Shi Zheng, Xiaojuan Wang, Tao Xiang, and Shaogang Gong. Multi-scale learning for low-resolution person re-identification. In 2015 IEEE International Conference on Computer Vision (ICCV), pp.\ 3765--3773, 2015. doi:10.1109/ICCV.2015.429

    2015 doi

  20. [29]

    Recover and identify: A generative dual model for cross-resolution person re-identification

    Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin, Xiaofei Du, and Yu-Chiang Frank Wang. Recover and identify: A generative dual model for cross-resolution person re-identification. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp.\ 8089--8098, 2019. doi:10.1109/ICCV...

    2019

  21. [31]

    Pose transferrable person re-identification

    Jinxian Liu, Bingbing Ni, Yichao Yan, Peng Zhou, Shuo Cheng, and Jianguo Hu. Pose transferrable person re-identification. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 4099--4108, 2018. doi:10.1109/CVPR.2018.00431

    2018

  22. [32]

    Aggregating deep pyramidal representations for person re-identification

    Niki Martinel, Gian Luca Foresti, and Christian and Micheloni. Aggregating deep pyramidal representations for person re-identification. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.\ 1544--1554, 2019. doi:10.1109/CVPRW.2019.00196

    2019

  23. [33]

    Linguistic regularities in continuous space word representations

    Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig. Linguistic regularities in continuous space word representations. In Lucy Vanderwende, Hal Daum \'e III, and Katrin Kirchhoff (eds.), Proceedings of the 2013 Conference of the North A merican Chapter of the Association for Comput...

    2013

  24. [34]

    Fact finding: Attempting to reverse-engineer factual recall on the neuron level

    Nanda Neel, Rajamanoharan Senthooran, Kramár János, and Shah Rohin. Fact finding: Attempting to reverse-engineer factual recall on the neuron level. URL https://www.alignmentforum.org/s/hpWHhjvjn67LJ4xXX/p/iGuwZTHWb6DFY3sKB

  25. [35]

    Person recognition system based on a combination of body images from visible light and thermal cameras

    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 0 (3), 2017. ISSN 1424-8220. doi:10.3390/s17030605. URL https://www.mdpi.com/1424-8220/17/3/605

    2017 doi

  26. [36]

    Median stable clustering and global distance classification for cross-domain person re-identification

    Zhiqi Pang, Jifeng Guo, Zhiqiang Ma, Wenbo Sun, and Yanbang Xiao. Median stable clustering and global distance classification for cross-domain person re-identification. IEEE Transactions on Circuits and Systems for Video Technology, 32 0 (5): 0 3164--3177, 2022. doi:10.1109/TC...

    2022

  27. [37]

    Dual-resolution fusion modeling for unsupervised cross-resolution person re-identification

    Zhiqi Pang, Lingling Zhao, and Chunyu Wang. Dual-resolution fusion modeling for unsupervised cross-resolution person re-identification. In Proceedings of the 32nd ACM International Conference on Multimedia, MM '24, pp.\ 4063–4072, New York, NY, USA, 2024. Association for Compu...

    2024

  28. [38]

    A novel image restoration solution for cross-resolution person re-identification

    Houfu Peng, Xing Lu, Xia Daoxun, and Xiaoyao Xie. A novel image restoration solution for cross-resolution person re-identification. 2024. doi:10.21203/rs.3.rs-3594420/v1

    2024 doi

  29. [39]

    Implicit discriminative knowledge learning for visible-infrared person re-identification

    Kaijie Ren and Lei Zhang. Implicit discriminative knowledge learning for visible-infrared person re-identification. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 393--402, 2024. URL https://api.semanticscholar.org/CorpusID:268531822

    2024

  30. [40]

    Person re-identification with deep similarity-guided graph neural network

    Yantao Shen, Hongsheng Li, Shuai Yi, Dapeng Chen, and Xiaogang Wang. Person re-identification with deep similarity-guided graph neural network. In European Conference on Computer Vision, 2018. URL https://api.semanticscholar.org/CorpusID:50785503

    2018

  31. [41]

    Nformer: Robust person re-identification with neighbor transformer

    Haochen Wang, Jiayi Shen, Yongtuo Liu, Yan Gao, and Efstratios Gavves. Nformer: Robust person re-identification with neighbor transformer. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 7287--7297, 2022. doi:10.1109/CVPR52688.2022.00715

    2022

  32. [42]

    Scale-adaptive low-resolution person re-identification via learning a discriminating surface

    Zheng Wang, Ruimin Hu, Yi Yu, Junjun Jiang, Chao Liang, and Jinqiao Wang. Scale-adaptive low-resolution person re-identification via learning a discriminating surface. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI'16, pp.\ ...

    2016

  33. [43]

    Cascaded sr-gan for scale-adaptive low resolution person re-identification

    Zheng Wang, Mang Ye, Fan Yang, Xiang Bai, and Shin'Ichi Satoh. Cascaded sr-gan for scale-adaptive low resolution person re-identification. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI'18, pp.\ 3891–3897. AAAI Press, 2018. ISBN 978...

    2018

  34. [44]

    Syncretic modality collaborative learning for visible infrared person re-identification

    Ziyu Wei, Xi Yang, Nannan Wang, and Xinbo Gao. Syncretic modality collaborative learning for visible infrared person re-identification. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp.\ 225--234, 2021. doi:10.1109/ICCV48922.2021.00029

    2021

  35. [45]

    Flexible body partition-based adversarial learning for visible infrared person re-identification

    Ziyu Wei, Xi Yang, Nannan Wang, and Xinbo Gao. Flexible body partition-based adversarial learning for visible infrared person re-identification. IEEE Transactions on Neural Networks and Learning Systems, 33 0 (9): 0 4676--4687, 2022. doi:10.1109/TNNLS.2021.3059713

    2022

  36. [46]

    Few-shot deep adversarial learning for video-based person re-identification

    Lin Wu, Yang Wang, Hongzhi Yin, Meng Wang, and Ling Shao. Few-shot deep adversarial learning for video-based person re-identification. IEEE Transactions on Image Processing, 29: 0 1233--1245, 2019. URL https://api.semanticscholar.org/CorpusID:88522997

    2019

  37. [47]

    Deep coattention-based comparator for relative representation learning in person re-identification

    Lin Wu, Yang Wang, Junbin Gao, Meng Wang, Zheng-Jun Zha, and Dacheng Tao. Deep coattention-based comparator for relative representation learning in person re-identification. IEEE Transactions on Neural Networks and Learning Systems, 32 0 (2): 0 722--735, 2021. doi:10.1109/TNNL...

    2021

  38. [48]

    Pseudo-pair based self-similarity learning for unsupervised person re-identification

    Lin Wu, Deyin Liu, Wenying Zhang, Dapeng Chen, Zongyuan Ge, Farid Boussaid, Mohammed Bennamoun, and Jialie Shen. Pseudo-pair based self-similarity learning for unsupervised person re-identification. IEEE Transactions on Image Processing, 31: 0 4803--4816, 2022. doi:10.1109/TIP...

    2022

  39. [49]

    Learning resolution-adaptive representations for cross-resolution person re-identification

    Lin Yuanbo Wu, Lingqiao Liu, Yang Wang, Zheng Zhang, Farid Boussaid, Mohammed Bennamoun, and Xianghua Xie. Learning resolution-adaptive representations for cross-resolution person re-identification. IEEE Transactions on Image Processing, 32: 0 4800--4811, 2023. doi:10.1109/TIP...

    2023

  40. [50]

    Mang Ye, Jianbing Shen, Gaojie Lin, Tao Xiang, Ling Shao, and Steven C. H. Hoi. Deep learning for person re-identification: A survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44 0 (6): 0 2872--2893, 2022 a . doi:10.1109/TPAMI.2021.3054775

    2022

  41. [51]

    Dynamic feature pruning and consolidation for occluded person re-identification

    Yuteng Ye, Hang Zhou, Junqing Yu, Qiang Hu, and Wei Yang. Dynamic feature pruning and consolidation for occluded person re-identification. In AAAI Conference on Artificial Intelligence, 2022 b . URL https://api.semanticscholar.org/CorpusID:254044700

    2022

  42. [52]

    A real-time memory updating strategy for unsupervised person re-identification

    Junhui Yin, Xinyu Zhang, Zhanyu Ma, Jun Guo, and Yifan Liu. A real-time memory updating strategy for unsupervised person re-identification. IEEE Transactions on Image Processing, 32: 0 2309--2321, 2023. doi:10.1109/TIP.2023.3266166

    2023

  43. [53]

    Jointly adaptive cross-resolution person re-identification on super-resolution

    Caihong Yuan, Zhijie Guan, Yuanchen Xu, Xiaopan Chen, Xiaoke Zhu, and Wenjuan Liang. Jointly adaptive cross-resolution person re-identification on super-resolution. Complex & Intelligent Systems, 11, 04 2025 a . doi:10.1007/s40747-025-01881-1

    2025 doi

  44. [54]

    From poses to identity: Training-free person re-identification via feature centralization

    Chao Yuan, Guiwei Zhang, Changxiao Ma, Tianyi Zhang, and Guanglin Niu. From poses to identity: Training-free person re-identification via feature centralization. 2025 b . URL https://api.semanticscholar.org/CorpusID:276741857

    2025

  45. [55]

    Neighbor-based feature and index enhancement for person re-identification

    Chao Yuan, Tianyi Zhang, and Guanglin Niu. Neighbor-based feature and index enhancement for person re-identification. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp.\ 5762--5769, 2025 c

    2025

  46. [56]

    Deep high-resolution representation learning for cross-resolution person re-identification

    Guoqing Zhang, Yu Ge, Zhicheng Dong, Hao Wang, Yuhui Zheng, and Shengyong Chen. Deep high-resolution representation learning for cross-resolution person re-identification. IEEE Transactions on Image Processing, 30: 0 8913--8925, 2021. doi:10.1109/TIP.2021.3120054

    2021

  47. [57]

    Scalable person re-identification: A benchmark

    Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re-identification: A benchmark. In 2015 IEEE International Conference on Computer Vision (ICCV), pp.\ 1116--1124, 2015. doi:10.1109/ICCV.2015.133

    2015 doi

  48. [58]

    Joint bilateral-resolution identity modeling for cross-resolution person re-identification

    Wei Shi Zheng, Jincheng Hong, Jiening Jiao, Ancong Wu, and Jianhuang Lai. Joint bilateral-resolution identity modeling for cross-resolution person re-identification. International Journal of Computer Vision, 130 0 (8), 2022

    2022

  49. [59]

    Unlabeled samples generated by gan improve the person re-identification baseline in vitro

    Zhedong Zheng, Liang Zheng, and Yi Yang. Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In 2017 IEEE International Conference on Computer Vision (ICCV), pp.\ 3774--3782, 2017. doi:10.1109/ICCV.2017.405

    2017 doi

  50. [60]

    Camera style adaptation for person re-identification

    Zhun Zhong, Liang Zheng, Zhedong Zheng, Shaozi Li, and Yi Yang. Camera style adaptation for person re-identification. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 5157--5166, 2018. doi:10.1109/CVPR.2018.00541

    2018

  51. [61]

    Person reidentification via multi-feature fusion with adaptive graph learning

    Runwu Zhou, Xiaojun Chang, Lei Shi, Yi-Dong Shen, Yi Yang, and Feiping Nie. Person reidentification via multi-feature fusion with adaptive graph learning. IEEE Transactions on Neural Networks and Learning Systems, 31 0 (5): 0 1592--1601, 2020. doi:10.1109/TNNLS.2019.2920905

    2020

  52. [62]

    Dssl: Deep surroundings-person separation learning for text-based person retrieval

    Aichun Zhu, Zijie Wang, Yifeng Li, Xili Wan, Jing Jin, Tian Wang, Fangqiang Hu, and Gang Hua. Dssl: Deep surroundings-person separation learning for text-based person retrieval. Proceedings of the 29th ACM International Conference on Multimedia, 2021. URL https://api.semantics...

    2021

  53. [63]

    Intra-camera supervised person re-identification: A new benchmark

    Xiangping Zhu, Xiatian Zhu, Minxian Li, Vittorio Murino, and Shaogang Gong. Intra-camera supervised person re-identification: A new benchmark. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp.\ 1079--1087, 2019. URL https://api.semanticscholar.org...

    2019

  54. [64]

    @esa (Ref

    \@ifxundefined[1] #1\@undefined \@firstoftwo \@secondoftwo \@ifnum[1] #1 \@firstoftwo \@secondoftwo \@ifx[1] #1 \@firstoftwo \@secondoftwo [2] @ #1 \@temptokena #2 #1 @ \@temptokena \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should ...

  55. [65]

    \@lbibitem[] @bibitem@first@sw\@secondoftwo \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 @tmp #1 NAT@b@open@#2 NAT@b@shut@#2 \@ifnum @merge>\@ne @bibitem@firs...

  56. [66]

    ZwwoOw/7CG | 3wOO n ϵ`_ xrsVq5W ߪ F<w vɆoc=. =F릥KO g`# ^'k0 ; xCۯ'1]= z `M ;Bb 4 ij jjU478 7ׇWm C8:z` B'g|i 7C sR 9?=Z!O/ 7 ϟ 5a_ooO

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifxundefined @sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifxundefined @heading @heading NAT@ctr thebibliography [1] @ \@biblabel @NAT@ctr \@bibset...

    1946

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.