Pith. sign in

REVIEW 4 major objections 7 minor 1 cited by

Feature Complementation Architecture for Visual Place Recognition

T0 review · 4 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read A parallel CNN-ViT network with dynamic fusion sets new recall records on seven visual place recognition benchmarks.

desk verdict Strong benchmark numbers with a core fusion equation that cannot implement the claimed CNN-ViT complementation; possibly a typo, but no code and several internal inconsistencies make the central claim unverifiable. read the letter →

arxiv 2506.12401 v1 pith:34ZDW4FU submitted 2025-06-14 cs.CV

Weiwei Wang , Meijia Wang , Haoyi Wang , Wenqiang Guo , Jiapan Guo , Changming Sun , Lingkun Ma , Weichuan Zhang This is my paper
classification cs.CV
keywords VisualplacerecognitionLocal-globalfeaturefusionDynamicVisionTransformerCNN-ViThybridFrequency-spatialadapterRecall@1Parameter-efficientfine-tuning
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 proposes LGCN, a visual place recognition network that runs a CNN and a vision Transformer in parallel and fuses their features with a learned dynamic weighting instead of a static rule. It claims this local-global complementation lets a single descriptor capture both fine-grained texture and global scene context, overcoming a limitation of prior single-architecture methods. On benchmarks, the method reports state-of-the-art Recall@1 scores of 95.0% on Pitts30k, 95.5% on Pitts250k, 93.3% on SPED, and strong results on cross-season and cross-domain datasets. The practical significance, if true, is that frozen ViT backbones can be adapted with lightweight adapters and hybrid fusion to substantially improve robot localization and navigation accuracy without retraining a large model.

What carries the argument

The two load-bearing mechanisms are the dynamic feature fusion module (DFM) and the frequency-spatial fusion adapter (FSA). DFM computes per-pixel, per-channel attention from the sum of the CNN and ViT feature maps using two 1x1 convolutions with ReLU and sigmoid, then recombines the ViT features with a learnable scaling factor so the network can emphasize different branches in different image regions. FSA is a lightweight adapter inserted into frozen ViT blocks: a frequency branch applies a 2D FFT, modulates the amplitude spectrum, and returns via inverse FFT, while a spatial branch uses depthwise convolutions; their outputs are added as a residual to the ViT output. The final descriptor is produced by feeding the fused features into a cross-image correlation-aware module, a component taken from the CricaVPR method.

What would settle it

Re-run each baseline from Tables 2 and 3 under the exact GSV-Cities training split, query/database partition, and Recall@N metrics used here, and check whether the Recall@1 margins persist; if the margins shrink to near zero, the claim of significant outperformance fails. Alternatively, replace the DFM with fixed-ratio fusion of the same two streams; if Recall@1 does not drop materially on Pitts30k, SPED, and MSLS val, then the dynamic mechanism is not the cause of the gains.

Watch

Extended reading notes

Core claim

The paper claims that the best VPR descriptors come from jointly exploiting a CNN's local detail sensitivity and a ViT's global context, combined by a learned dynamic weighting rather than a fixed-ratio sum. Concretely, ResNet-50 features are upsampled to match a frozen DINOv2-pretrained ViT-B feature map, the two are added, and a bottleneck gating sub-network generates spatial-channel attention weights modulated by a learnable global scaling factor. In parallel, frequency-spatial adapters inserted after the ViT's attention layers modulate the amplitude spectrum in the frequency domain and apply depthwise convolutions in the spatial domain, adding local inductive bias without unfreezing the backbone. The method reports Recall@1 of 95.0% on Pitts30k, 95.5% on Pitts250k, 93.3% on SPED, 91.0% on MSLS val, and 89.8% on Nordland, with ablations attributing the gains to the adapter, the CNN stream, and the dynamic fusion module.

Load-bearing premise

The reported gains over prior work rest on the assumption that the baseline numbers in Tables 2 and 3 were produced under the same training data, evaluation splits, and re-ranking conditions as LGCN, since no baseline is re-run in this paper.

Editorial extensions

If this is right

  • If LGCN is right, combining a frozen ViT with a CNN through learned dynamic fusion is a practical recipe for VPR accuracy, reaching 95.0% Recall@1 on Pitts30k and 95.5% on Pitts250k.
  • The ablation results imply each component is individually useful: adding FSA to the frozen ViT baseline improves Recall@1 by 6.2% on Pitts30k, and adding the CNN stream and DFM each give further gains.
  • Dynamic weighted fusion is claimed to outperform static concatenation and fixed-ratio summation, since the +DFM variant beats the +CNN Stream variant that uses simple concatenation.
  • The method's strong performance on SPED, Nordland, and St-Lucia suggests that frequency-domain adaptation and hybrid fusion generalize across seasonal, low-texture, and dynamic urban scenes.
  • Because the ViT backbone stays frozen and only lightweight adapters and fusion parameters are trained, the approach is parameter-efficient relative to full fine-tuning.

Reading between the lines

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

  • Editorial inference: Eq. (5) as written multiplies both recombined branches by the ViT feature map, so the CNN stream's contribution enters only through the summed feature F used to generate gating weights; the prose claims the fusion enhances CNN features in texture-rich regions, but the equations do not explicitly show a separate weighted CNN term. A reader should verify whether the intended fus
  • Editorial inference: The reported margins over prior work may be sensitive to evaluation protocol, since no baseline is re-run under the exact same training split and query/database partition; an independent re-implementation with identical protocol would test whether the gains hold.
  • Editorial inference: The frequency-spatial adapter idea could transfer to other retrieval tasks that need invariance to illumination and seasonal change, such as long-term visual SLAM or image-based re-localization under weather variation, which are not tested in this paper.
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 / 7 minor

Summary. This paper proposes LGCN, a visual place recognition network combining a frozen DINOv2 ViT-Base branch and a frozen DINO-pretrained ResNet-50 branch. A dynamic feature fusion module (DFM) is introduced to recombine the two branches through a gating mechanism, and frequency-spatial adapters (FSA) are inserted into the frozen ViT to add local inductive bias. A cross-image correlation-aware module borrowed from CricaVPR is used on top of the fused features to produce the final descriptor. The method is evaluated on seven VPR benchmarks (Pitts30k, Pitts250k, SPED, MSLS val, Nordland, Eynsham, St-Lucia) and reported to achieve state-of-the-art R@1 on most of them. The central claim is that the CNN-ViT feature complementation through DFM is responsible for the accuracy gains.

Significance. If the claimed mechanism were realized as described, the paper would address a real and active problem: combining local CNN features and global ViT features for visual place recognition. The use of parameter-efficient adapters for a frozen backbone is also timely. The evaluation is broad and follows the common GSV-Cities training protocol, so the empirical comparison is not circular. However, the significance is substantially undermined because the core fusion equations, as written, do not implement the advertised CNN-ViT complementation, and no code or corrected specification is supplied. The contribution relative to CricaVPR is also not clearly demarcated, since the cross-image module is adopted from that work.

major comments (4)
  1. [Section 3.2, Eqs. (3)-(5)] The DFM as specified does not fuse CNN features into the output. Eq. (3) forms F = F_ViT + F'_Res and Eq. (4) computes the gate ω from F, but Eq. (5) defines Ffused1 = ω⊙F_ViT and Ffused2 = (1−ω)⊙F_ViT. Both fused outputs are functions only of F_ViT; F'_Res appears only inside ω, where it can modulate a gate but cannot contribute any feature content. The text at the same point claims that the mechanism 'enhanc[es] the contribution of CNN features in regions rich in texture,' which Eq. (5) cannot do. The learnable parameters α1 and α2 are introduced in the same section but are never used in any equation. Because the DFM is the central contribution named in the title, abstract, and ablation study, this is not a cosmetic typo: the paper does not provide a correct, checkable specification of its core mechanism, and the +DFM row in Table 4 cannot validate a fusion that the equations do not describe.
  2. [Section 4.3 / Section 4.4, Tables 2 and 4] The reported R@1 for the full model on Pitts30k is inconsistent across tables: Table 2 lists 95.0 for 'Ours' while Table 4 lists 95.5 for the 'Full Model (Ours)'. The paper also claims in the abstract that LGCN 'consistently outperforms' prior methods, but Table 2 shows BoQ achieving a higher R@1 on MSLS val (91.4 vs 91.0) and Table 3 shows BoQ tied on St-Lucia (99.9). These discrepancies and exceptions need to be stated precisely and reconciled.
  3. [Section 4.3, Table 2] The claim of 'significantly outperforming' is not supported by the reported margins. Against CricaVPR on Pitts30k the improvement is 0.1 percentage point and against SelaVPR on Pitts250k it is 0.3 point. No standard deviations, multiple-run statistics, or significance tests are reported, so these differences may be within run-to-run variation. Please either soften the wording or provide statistical evidence.
  4. [Section 3.1 / Section 4.2] The architecture description is internally inconsistent about the ViT patch size. Section 3.1 states that input images are divided into 16×16 patches and that F_ViT has shape B×16×16×768, while Section 4.2 says the backbone is ViT-B/14. A 224×224 input with patch size 14 yields 16×16 tokens, so the shape is consistent with a patch size of 14, not 16. Please clarify which ViT variant is used and make the patch-size statements consistent, since this affects reproducibility.
minor comments (7)
  1. [Section 3.2, Eq. (4)] The notation switches between w1/w2 in the equation and ω1/ω2 in the text; please use consistent symbols.
  2. [Section 2.2] The sentence 'BoQ [43] designed learnable global query vectors' cites reference [43], which in the same paragraph is CricaVPR; the correct citation is [63]. This also affects the related-work narrative.
  3. [Section 4.2 / Fig. 4] The cross-image correlation-aware module is adopted from CricaVPR, but the paper does not clearly state in the method section that this is a borrowed component rather than a new contribution; please mark the provenance in Fig. 4 and its surrounding text.
  4. [Section 4.2] Training details are incomplete: no loss function, number of epochs, or evaluation protocol for the additional datasets (Nordland, Eynsham, St-Lucia) are given, so the reader cannot reproduce the results.
  5. [Fig. 2] The bar chart in Fig. 2 is not referenced in the text, and its caption lists SPED but not the other datasets; please add a reference and complete the caption.
  6. [Fig. 6] The caption mentions 'SFRS' as a compared method, but SFRS is not described or listed in the baselines.
  7. [Section 3.3] The frequency-spatial adapter is described only in words; no equations or parameter counts are given, making it difficult to assess the claimed 'lightweight' property.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the benchmark results are external empirical measurements, and the DFM/FSA mechanisms do not reduce the predictions to their own inputs.

full rationale

The paper's central claim is empirical: LGCN is trained on GSV-Cities and evaluated on the standard Pitts30k, Pitts250k, SPED, MSLS, Nordland, Eynsham, and St-Lucia benchmarks, with Recall@N numbers compared against published baselines under the same benchmark protocol. No parameter is fitted to the reported Recall@1 values, and no uniqueness theorem or prior result by the same authors is invoked to force the architecture. The cross-image module is explicitly borrowed from CricaVPR ([43], prior independent work by Lu et al.), and the frequency-spatial adapter is presented with its own equations, with the self-citations in [52-54] used only as inspiration rather than as load-bearing proof. The ablation study compares variants on the same external test sets, so the attributed gains are not definitional. One in-scope concern is flagged but is not circularity: in Section 3.2, Eqs. (3)-(5) define F = F_ViT + F'_Res, then F_fused1 = omega⊙F_ViT and F_fused2 = (1-omega)⊙F_ViT, so both fused outputs are functions of the ViT branch only, with the CNN branch entering only through the gate omega computed in Eq. (4); this contradicts the text's claim that the module enhances 'the contribution of CNN features in regions rich in texture.' That is an internal specification or soundness defect (possibly a typo omitting F'_Res, and alpha1/alpha2 are never used), but it does not make the benchmark results equivalent to the model's inputs or to fitted constants. Therefore the circularity score is 0.

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

The method relies on standard building blocks and a set of unexamined hyperparameters. No new physical entities are introduced. The central mechanism, as expressed in Eq. (5), fails to include the CNN branch in the fused output, which is a mathematical error rather than a freely chosen parameter.

free parameters (4)
  • learnable fusion scalars alpha_1, alpha_2 = not reported
    In Section 3.2, alpha_1 and alpha_2 are introduced to balance CNN and ViT contributions and are optimized via backpropagation; final values are never given.
  • channel compression ratio 1/4 in DFM = 768 to 192 reduction
    Section 3.2 sets w1 to reduce channels by a factor of 4 to save computation; no ablation justifies this ratio.
  • adapter bottleneck dimensions and layer count = not reported
    The FSA module sizes are not specified in Section 3.3 or Section 4.2, leaving the parameter overhead claim unquantified.
  • learning rate and batch size = 1e-5, batch 16
    Section 4.2 states these values without sensitivity analysis.
assumptions (4)
  • standard math Standard self-attention and Fourier transform mathematics
    Used in Eq. (1) and the FSA frequency branch; assumed correct.
  • domain assumption DINOv2 and DINO pre-trained features transfer to place recognition
    Section 3.1 chooses frozen DINOv2 ViT and DINO ResNet-50 without evidence specific to VPR.
  • domain assumption GSV-Cities training generalizes to the evaluation benchmarks
    Section 4.2 fine-tunes only on GSV-Cities and evaluates on Pitts30k, SPED, MSLS, Nordland, and others; overlap or distribution shift is not analyzed.
  • domain assumption The 10 m / 25 m positive-negative threshold defines correct place matches
    Section 4.2 defines positives within 10 m and negatives beyond 25 m without justification.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Feature Complementation Architecture for Visual Place Recognition." pith.science (2026). https://pith.science/paper/34ZDW4FU

@misc{pith2026250612401,
  author       = {Pith},
  title        = {Pith review of: Feature Complementation Architecture for Visual Place Recognition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/34ZDW4FU}},
  note         = {Machine review of arXiv:2506.12401}
}
read the original abstract

Visual place recognition (VPR) plays a crucial role in robotic localization and navigation. The key challenge lies in constructing feature representations that are robust to environmental changes. Existing methods typically adopt convolutional neural networks (CNNs) or vision Transformers (ViTs) as feature extractors. However, these architectures excel in different aspects -- CNNs are effective at capturing local details. At the same time, ViTs are better suited for modeling global context, making it difficult to leverage the strengths of both. To address this issue, we propose a local-global feature complementation network (LGCN) for VPR which integrates a parallel CNN-ViT hybrid architecture with a dynamic feature fusion module (DFM). The DFM performs dynamic feature fusion through joint modeling of spatial and channel-wise dependencies. Furthermore, to enhance the expressiveness and adaptability of the ViT branch for VPR tasks, we introduce lightweight frequency-to-spatial fusion adapters into the frozen ViT backbone. These adapters enable task-specific adaptation with controlled parameter overhead. Extensive experiments on multiple VPR benchmark datasets demonstrate that the proposed LGCN consistently outperforms existing approaches in terms of localization accuracy and robustness, validating its effectiveness and generalizability.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

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

  1. SAGE: Spatial-visual Adaptive Graph Exploration for Efficient Visual Place Recognition

    cs.CV 2025-09 conditional novelty 5.0 of 10

    SAGE is a training pipeline that dynamically rebuilds a geo-visual graph and uses greedy clique sampling and soft local-feature weighting to reach state-of-the-art visual place recognition on eight benchmarks.

Reference graph

Works this paper leans on

63 extracted references · 58 canonical work pages · cited by 1 Pith paper

  1. [1]

    In: IEEE International Conference on Computer Vision, pp

    Liu, L., Li, H., Dai, Y.: Stochastic attraction-repulsion embedding for large scale image localization. In: IEEE International Conference on Computer Vision, pp. 2570–2579 (2019)

    work page 2019

  2. [2]

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

    Jin Kim, H., Dunn, E., Frahm, J.-M.: Learned contextual feature reweighting for image geo-localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2136–2145 (2017)

    work page 2017

  3. [3]

    Computer Vision and Image Understanding, 104411 (2025)

    Tang, X., Cen, S., Deng, Z., Zhang, Z., Meng, Y., Xie, J., Tang, C., Zhang, W., Zhao, G.: Cascading attention enhancement network for RGB-D indoor scene segmentation. Computer Vision and Image Understanding, 104411 (2025)

    work page 2025

  4. [4]

    Pattern Recognition160, 111174 (2025)

    Guo, X., Jiang, F., Chen, Q., Wang, Y., Sha, K., Chen, J.: Deep learning- enhanced environment perception for autonomous driving: MDNet with CSP- DarkNet53. Pattern Recognition160, 111174 (2025)

    work page 2025

  5. [5]

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

    Doan, A.-D., Latif, Y., Chin, T.-J., Liu, Y., Do, T.-T., Reid, I.: Scalable place recognition under appearance change for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9319–9328 (2019)

    work page 2019

  6. [6]

    Pattern Recognition165, 111607 (2025)

    Liao, Y., Gao, Y., Zhang, W.: Dynamic accumulated attention map for interpret- ing evolution of decision-making in vision transformer. Pattern Recognition165, 111607 (2025)

    work page 2025

  7. [7]

    Neurocomputing503, 259–271 (2022)

    Jing, J., Liu, S., Wang, G., Zhang, W., Sun, C.: Recent advances on image edge detection: A comprehensive review. Neurocomputing503, 259–271 (2022)

    work page 2022

  8. [8]

    International Journal of Computer Vision128(2), 438–459 (2020)

    Zhang, W., Sun, C.: Corner detection using multi-directional structure tensor with multiple scales. International Journal of Computer Vision128(2), 438–459 (2020)

    work page 2020

Show all 63 references
  1. [9]

    In: European Conference on Computer Vision, pp

    Middelberg, S., Sattler, T., Untzelmann, O., Kobbelt, L.: Scalable 6-DOF local- ization on mobile devices. In: European Conference on Computer Vision, pp. 268–283 (2014)

    2014

  2. [10]

    In: European Conference on Computer Vision, pp

    Cao, B., Araujo, A., Sim, J.: Unifying deep local and global features for image search. In: European Conference on Computer Vision, pp. 726–743 (2020) 16

    2020

  3. [11]

    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

  4. [12]

    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)

    2020 arXiv

  5. [13]

    Advances in Neural Information Processing Systems25(2012)

    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems25(2012)

    2012

  6. [14]

    arXiv preprint arXiv:1409.1556 (2014)

    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

    2014 arXiv

  7. [15]

    IEEE Transactions on Pattern Analysis and Machine Intelligence 45(8), 9883–9894 (2023)

    Zhang, W., Sun, C., Gao, Y.: Image intensity variation information for interest point detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 45(8), 9883–9894 (2023)

    2023

  8. [16]

    IEEE Transactions on Pattern Analysis and Machine Intelligence45(4), 4694–4712 (2022)

    Jing, J., Gao, T., Zhang, W., Gao, Y., Sun, C.: Image feature information extrac- tion for interest point detection: A comprehensive review. IEEE Transactions on Pattern Analysis and Machine Intelligence45(4), 4694–4712 (2022)

    2022

  9. [17]

    Pattern Recognition 148, 110158 (2024)

    Zhang, W., Zhao, Y., Gao, Y., Sun, C.: Re-abstraction and perturbing support pair network for few-shot fine-grained image classification. Pattern Recognition 148, 110158 (2024)

    2024

  10. [18]

    Mathematics13(7), 1098 (2025)

    Wang, M., Zheng, B., Wang, G., Yang, J., Lu, J., Zhang, W.: A principal compo- nent analysis-based feature optimization network for few-shot fine-grained image classification. Mathematics13(7), 1098 (2025)

    2025

  11. [19]

    IEEE Transactions on Image Processing28(9), 4444–4459 (2019)

    Zhang, W., Sun, C., Breckon, T., Alshammari, N.: Discrete curvature represen- tations for noise robust image corner detection. IEEE Transactions on Image Processing28(9), 4444–4459 (2019)

    2019

  12. [20]

    Sensors24(23), 7737 (2024)

    Wang, J., Lu, J., Yang, J., Wang, M., Zhang, W.: An unbiased feature estima- tion network for few-shot fine-grained image classification. Sensors24(23), 7737 (2024)

    2024

  13. [21]

    IEEE Transactions on Radia- tion and Plasma Medical Sciences (2024)

    Lei, T., Song, W., Zhang, W., Du, X., Li, C., He, L., Nandih, A.K.: Semi- Supervised 3D Medical Image Segmentation Using Multi-Consistency Learning With Fuzzy Perception-Guided Target Selection. IEEE Transactions on Radia- tion and Plasma Medical Sciences (2024)

    2024

  14. [22]

    IEEE Access11, 17 121688–121701 (2023)

    Lu, J., Peng, G., Zhang, W., Sun, C.: Track-before-detect algorithm based on cost-reference particle filter bank for weak target detection. IEEE Access11, 17 121688–121701 (2023)

    2023

  15. [23]

    Pattern Recognition63, 193–205 (2017)

    Zhang, W., Zhao, Y., Breckon, T.P., Chen, L.: Noise robust image edge detection based upon the automatic anisotropic Gaussian kernels. Pattern Recognition63, 193–205 (2017)

    2017

  16. [24]

    IET Image Processing8(11), 639–646 (2014)

    Zhang, W.-C., Wang, F.-P., Zhu, L., Zhou, Z.-F.: Corner detection using Gabor filters. IET Image Processing8(11), 639–646 (2014)

    2014

  17. [25]

    no (2022)

    Jing, J., Gao, T., Zhang, W., Gao, Y., Sun, C.: Image feature information extraction for interest point detection: A review. no (2022)

    2022

  18. [26]

    IEEE Transactions on Image Processing 22(8), 3204–3218 (2013)

    Shui, P.-L., Zhang, W.-C.: Corner detection and classification using anisotropic directional derivative representations. IEEE Transactions on Image Processing 22(8), 3204–3218 (2013)

    2013

  19. [27]

    Pattern Recognition48(9), 2785–2797 (2015)

    Zhang, W.-C., Shui, P.-L.: Contour-based corner detection via angle difference of principal directions of anisotropic gaussian directional derivatives. Pattern Recognition48(9), 2785–2797 (2015)

    2015

  20. [28]

    arXiv preprint arXiv:2106.06988 (2021)

    Zhang, W., Liu, X., Xue, Z., Gao, Y., Sun, C.: NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification. arXiv preprint arXiv:2106.06988 (2021)

    2021 arXiv

  21. [29]

    AI5(1), 405–425 (2024)

    Ren, J., Li, C., An, Y., Zhang, W., Sun, C.: Few-shot fine-grained image classification: A comprehensive review. AI5(1), 405–425 (2024)

    2024

  22. [30]

    Interna- tional Journal of Computer Vision60, 91–110 (2004)

    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Interna- tional Journal of Computer Vision60, 91–110 (2004)

    2004

  23. [31]

    In: European Conference on Computer Vision, pp

    Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. In: European Conference on Computer Vision, pp. 404–417 (2006). Springer

    2006

  24. [32]

    In: International Conference on Computer Vision, pp

    Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: ORB: An efficient alternative to SIFT or SURF. In: International Conference on Computer Vision, pp. 2564– 2571 (2011)

    2011

  25. [33]

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

    Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5297–5307 (2016)

    2016

  26. [34]

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

    Hausler, S., Garg, S., Xu, M., Milford, M., Fischer, T.: Patch-NetVLAD: Multi- scale fusion of locally-global descriptors for place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 14141– 14152 (2021)

    2021

  27. [35]

    IEEE Transactions on Intelligent Transportation Systems 25(2), 1291–1302 (2023)

    Nie, J., Feng, J.-M., Xue, D., Pan, F., Liu, W., Hu, J., Cheng, S.: A training-free, lightweight global image descriptor for long-term visual place recognition toward 18 autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems 25(2), 1291–1302 (2023)

    2023

  28. [36]

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

    Berton, G., Masone, C., Caputo, B.: Rethinking visual geo-localization for large- scale applications. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4878–4888 (2022)

    2022

  29. [37]

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

    Ali-Bey, A., Chaib-Draa, B., Giguere, P.: MixVPR: Feature mixing for visual place recognition. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision, pp. 2998–3007 (2023)

    2023

  30. [38]

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

    Berton, G., Trivigno, G., Caputo, B., Masone, C.: EigenPlaces: Training view- point robust models for visual place recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 11080–11090 (2023)

    2023

  31. [39]

    Advances in Neural Information Processing Systems30(2017)

    Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, /suppress L., Polosukhin, I.: Attention is all you need. Advances in Neural Information Processing Systems30(2017)

    2017

  32. [40]

    IEEE Transactions on Pattern Analysis and Machine Intelligence41(7), 1655–1668 (2018)

    Radenovi´ c, F., Tolias, G., Chum, O.: Fine-tuning CNN image retrieval with no human annotation. IEEE Transactions on Pattern Analysis and Machine Intelligence41(7), 1655–1668 (2018)

    2018

  33. [41]

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

    Zhu, S., Yang, L., Chen, C., Shah, M., Shen, X., Wang, H.: R 2former: Unified retrieval and reranking transformer for place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 19370–19380 (2023)

    2023

  34. [42]

    arXiv preprint arXiv:2402.14505 (2024)

    Lu, F., Zhang, L., Lan, X., Dong, S., Wang, Y., Yuan, C.: Towards seamless adaptation of pre-trained models for visual place recognition. arXiv preprint arXiv:2402.14505 (2024)

    2024 arXiv

  35. [43]

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

    Lu, F., Lan, X., Zhang, L., Jiang, D., Wang, Y., Yuan, C.: CricaVPR: Cross-image correlation-aware representation learning for visual place recognition. In: Pro- ceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 16772–16782 (2024)

    2024

  36. [44]

    IEEE Robotics and Automation Letters9(2), 1286–1293 (2023)

    Keetha, N., Mishra, A., Karhade, J., Jatavallabhula, K.M., Scherer, S., Krishna, M., Garg, S.: AnyLoc: Towards universal visual place recognition. IEEE Robotics and Automation Letters9(2), 1286–1293 (2023)

    2023

  37. [45]

    In: International Conference on Artificial Neural Networks, pp

    Hu, J., Mao, C., Tan, C., Li, H., Liu, H., Zheng, M.: ProGEO: Generating prompts through image-text contrastive learning for visual geo-localization. In: International Conference on Artificial Neural Networks, pp. 448–462 (2024)

    2024

  38. [46]

    In: Proceedings of the IEEE 19 Winter Conference on Applications of Computer Vision, pp

    Zhang, H., Chen, X., Jing, H., Zheng, Y., Wu, Y., Jin, C.: ETR: An efficient transformer for re-ranking in visual place recognition. In: Proceedings of the IEEE 19 Winter Conference on Applications of Computer Vision, pp. 5665–5674 (2023)

    2023

  39. [47]

    IEEE Transactions on Circuits and Systems for Video Technology33(3), 1109–1122 (2022)

    Wang, Y., Qiu, Y., Cheng, P., Zhang, J.: Hybrid CNN-transformer features for visual place recognition. IEEE Transactions on Circuits and Systems for Video Technology33(3), 1109–1122 (2022)

    2022

  40. [48]

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

    Wang, R., Shen, Y., Zuo, W., Zhou, S., Zheng, N.: TransVPR: Transformer-based place recognition with multi-level attention aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 13648–13657 (2022)

    2022

  41. [49]

    In: International Conference on Machine Learning, pp

    Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Ges- mundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for NLP. In: International Conference on Machine Learning, pp. 2790–2799 (2019)

    2019

  42. [50]

    Advances in Neural Information Processing Systems35, 16664–16678 (2022)

    Chen, S., Ge, C., Tong, Z., Wang, J., Song, Y., Wang, J., Luo, P.: AdaptFormer: Adapting vision transformers for scalable visual recognition. Advances in Neural Information Processing Systems35, 16664–16678 (2022)

    2022

  43. [51]

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

    Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    2018

  44. [52]

    In: European Conference on Computer Vision, pp

    Li, H., Li, S., Ding, S., Dai, W., Cao, M., Li, C., Zou, J., Xiong, H.: Image compression for machine and human vision with spatial-frequency adaptation. In: European Conference on Computer Vision, pp. 382–399 (2024)

    2024

  45. [53]

    Scientific Reports15(1), 1–10 (2025)

    Ren, J., Zhao, Y., Zhang, W., Sun, C.: Zero-shot incremental learning using spatial-frequency feature representations. Scientific Reports15(1), 1–10 (2025)

    2025

  46. [54]

    Pattern Recognition155, 110686 (2024)

    Pan, Z., Zhang, W., Yu, X., Zhang, M., Gao, Y.: Pseudo-set frequency refine- ment architecture for fine-grained few-shot class-incremental learning. Pattern Recognition155, 110686 (2024)

    2024

  47. [55]

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

    Berton, G., Mereu, R., Trivigno, G., Masone, C., Csurka, G., Sattler, T., Caputo, B.: Deep visual geo-localization benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5396–5407 (2022)

    2022

  48. [56]

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

    Torii, A., Sivic, J., Pajdla, T., Okutomi, M.: Visual place recognition with repeti- tive structures. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 883–890 (2013)

    2013

  49. [57]

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

    Torii, A., Arandjelovic, R., Sivic, J., Okutomi, M., Pajdla, T.: 24/7 place recog- nition by view synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1808–1817 (2015)

    2015

  50. [58]

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

    Warburg, F., Hauberg, S., Lopez-Antequera, M., Gargallo, P., Kuang, Y., Civera, 20 J.: Mapillary street-level sequences: A dataset for lifelong place recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2626–2635 (2020)

    2020

  51. [59]

    In: Proceedings of Workshop on Long-term Autonomy, IEEE International Conference on Robotics and Automation, p

    S¨ underhauf, N., Neubert, P., Protzel, P.: Are we there yet? Challenging SeqS- LAM on a 3000 km journey across all four seasons. In: Proceedings of Workshop on Long-term Autonomy, IEEE International Conference on Robotics and Automation, p. 2013 (2013). Citeseer

    2013

  52. [60]

    Robotics: Science and Systems (2009)

    Cummins, M., Newman, P.: Highly scalable appearance-only SLAM–FAB-MAP 2.0. Robotics: Science and Systems (2009)

    2009

  53. [61]

    IEEE Transactions on Robotics24(5), 1038– 1053 (2008)

    Milford, M.J., Wyeth, G.F.: Mapping a suburb with a single camera using a biologically inspired SLAM system. IEEE Transactions on Robotics24(5), 1038– 1053 (2008)

    2008

  54. [62]

    Neurocomputing513, 194–203 (2022)

    Ali-bey, A., Chaib-draa, B., Gigu` ere, P.: GSV-Cities: Toward appropriate super- vised visual place recognition. Neurocomputing513, 194–203 (2022)

    2022

  55. [63]

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

    Ali-Bey, A., Chaib-draa, B., Gigu` ere, P.: BoQ: A place is worth a bag of learnable queries. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 17794–17803 (2024) 21

    2024

Pith tools

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