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arxiv: 2605.31597 · v2 · pith:VEMME5RSnew · submitted 2026-05-29 · 💻 cs.CV

SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models

Olaf D\"unkel , Basavaraj Sunagad , Haoran Wang , David T. Hoffmann , Christian Theobalt , Adam Kortylewski This is my paper

Pith reviewed 2026-06-28 23:00 UTC · model grok-4.3

classification 💻 cs.CV
keywords semantic correspondencevision foundation modelsbenchmarkkeypoint annotationsdownstream taskspart-level understandingLVLMsobject parts
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The pith

Semantic correspondence performance predicts dense downstream task success more strongly than ImageNet classification accuracy.

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

The paper presents SOCO, a benchmark supplying consistent keypoint annotations and language descriptions across 100 object categories and more than one million pairs. It uses the benchmark to test vision foundation models and large vision-language models on semantic object correspondence under appearance, viewpoint, and geometry changes. The results indicate that these models encode semantic structure yet transfer it poorly across categories and locate parts only partially. Large vision-language models locate parts more accurately from text prompts than from visual references. Correspondence accuracy correlates more strongly with results on segmentation, tracking, 3D pose estimation, and 3D detection than ImageNet classification accuracy does.

Core claim

SOCO supplies a taxonomy of correspondence types together with consistent, functionally meaningful keypoint annotations and language descriptions. Experiments on this benchmark show that vision foundation backbones encode strong semantic structure but transfer correspondences poorly across related categories and capture object-part position only partially. Large vision-language models perform better at text-prompted part localization than at visual-reference cross-image matching. Correspondence performance predicts performance on dense downstream tasks including segmentation, tracking, 3D pose estimation, and 3D detection more strongly than ImageNet classification.

What carries the argument

The SOCO benchmark, which defines a taxonomy of correspondence types, supplies over one million consistent keypoint pairs across 100 categories, and adds language descriptions for evaluating part-level understanding.

Load-bearing premise

The keypoint annotations are functionally meaningful, consistent across instances and categories, and capture the relevant variations in appearance, viewpoint, and geometry needed for fair evaluation.

What would settle it

Finding a model that achieves high scores on SOCO correspondence pairs yet low accuracy on segmentation, tracking, or 3D pose estimation (or the reverse pattern) would undermine the claim that correspondence is the stronger predictor.

Figures

Figures reproduced from arXiv: 2605.31597 by Adam Kortylewski, Basavaraj Sunagad, Christian Theobalt, David T. Hoffmann, Haoran Wang, Olaf D\"unkel.

Figure 1
Figure 1. Figure 1: SOCO provides the first taxonomy-driven, language-grounded formulation of Semantic Object Correspondence (SOC), enabling structured, semantically co￾herent, and cross-category part annotations across 100 diverse categories, which allows evaluating semantic and structured object understanding in vision foundation models (VFMs) and large vision language models (LVLMs). Abstract. Measuring structured object u… view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of concept correspondence (CC), semantic object cor [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Statistics of labeled keypoints. Keypoints in SOCO are annotated for a diverse set of categories from four super-categories. Each category is labeled with a subset of keypoints that are shared across multiple categories. The animal keypoints are shared across all animal categories. Image collection. All images are samples from ImageNet. We rely on 2D and 3D annotations from ImageNet3D [41] for man-made obj… view at source ↗
Figure 4
Figure 4. Figure 4: Per-task Pearson r across 37 vision models, with 95% bootstrap CIs. Left: SOC correlates with every downstream task more strongly than ImageNet kNN. Right: the SOC advantage ∆r = rSOC −rkNN stays positive on all tasks and is preserved on a 17 subset only including models trained with dense SSL objectives. Overall, recent models show clear improvements in both visual and lan￾guage understanding. For example… view at source ↗
read the original abstract

Measuring structured object understanding in vision foundation models remains challenging due to inconsistent evaluation protocols and limited part-level supervision. Semantic correspondence (SC) evaluates this capability by testing whether object parts can be matched across instances and categories under large variations in appearance, viewpoint, and geometry. To enable a systematic SC evaluation, we introduce SOCO, a new benchmark for Semantic Object Correspondence that introduces a taxonomy of correspondence types and provides consistent, functionally meaningful keypoint annotations across 100 categories and over 1M correspondence pairs. In addition, SOCO includes keypoint language descriptions, enabling the evaluation of large vision-language models (LVLMs) and their fine-grained part-level understanding. Comprehensive experiments reveal that (i) vision foundation backbones encode strong semantic structure but transfer correspondences poorly across related categories and only partially capture object-part position, (ii) LVLMs are stronger at text-prompted part localization than at visual-reference cross-image matching, exposing a gap between language-grounded localization and fine-grained visual correspondence, and (iii) correspondence performance predicts performance on dense downstream tasks, including segmentation, tracking, 3D pose estimation, and 3D detection, more strongly than ImageNet classification. Together, these findings position SOCO as a benchmark for structured, part-level representation quality in vision and multimodal foundation models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces the SOCO benchmark for semantic object correspondence, featuring a taxonomy of correspondence types, consistent keypoint annotations across 100 categories with over 1M pairs, and language descriptions for keypoints. It evaluates vision foundation models and LVLMs, reporting three findings: backbones encode semantic structure but transfer correspondences poorly across categories and only partially capture part positions; LVLMs perform better at text-prompted part localization than visual-reference matching; and correspondence performance predicts results on dense downstream tasks (segmentation, tracking, 3D pose, 3D detection) more strongly than ImageNet classification accuracy.

Significance. If the keypoint annotations and correlations prove robust, SOCO would provide a useful large-scale benchmark for part-level structured understanding in foundation models, with the predictive relation to downstream dense tasks offering a concrete alternative to ImageNet-centric evaluation. The inclusion of both visual and language-grounded evaluations and the scale of the dataset are clear strengths that could influence model assessment practices.

major comments (2)
  1. [§3] §3 (SOCO Benchmark): The central claim that SC performance predicts downstream task performance more strongly than ImageNet accuracy (finding iii) rests on the reliability of the 1M+ keypoint pairs being functionally meaningful and consistent. The manuscript states these properties but reports no inter-annotator agreement, expert validation study, or ablation perturbing the annotations to test sensitivity of the reported correlations. This is load-bearing for the strongest empirical claim.
  2. [§5.3] §5.3 (Downstream Correlation Analysis): The stronger predictive power of SC scores versus ImageNet accuracy is presented without details on pre-specification of category subsets, aggregation method across the 100 categories, or controls for category difficulty. Post-hoc selection could inflate the reported advantage; a concrete test (e.g., fixed held-out category split) is needed to support the claim.
minor comments (2)
  1. [§3.2] Figure 2 and §3.2: The taxonomy of correspondence types is introduced but the distribution of the 1M pairs across types is not quantified, making it hard to interpret transfer results across related categories.
  2. [§4.1] §4.1: Notation for SC metrics (e.g., PCK thresholds) should be defined before the first table of results for clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below and will incorporate revisions to strengthen the empirical foundations of our claims regarding annotation reliability and correlation analysis.

read point-by-point responses

  1. Referee: [§3] §3 (SOCO Benchmark): The central claim that SC performance predicts downstream task performance more strongly than ImageNet accuracy (finding iii) rests on the reliability of the 1M+ keypoint pairs being functionally meaningful and consistent. The manuscript states these properties but reports no inter-annotator agreement, expert validation study, or ablation perturbing the annotations to test sensitivity of the reported correlations. This is load-bearing for the strongest empirical claim.

    Authors: We agree that the absence of inter-annotator agreement metrics, expert validation, and sensitivity ablations represents a gap for a load-bearing claim. The annotations were created by multiple trained annotators using a detailed taxonomy and functional guidelines to promote consistency across the 100 categories. However, these validation steps were not included in the original manuscript. In the revised version, we will report inter-annotator agreement on a sampled subset of categories, include results from an expert validation study, and add a perturbation ablation that randomly alters a fraction of keypoints before recomputing the downstream correlations to assess sensitivity. revision: yes

  2. Referee: [§5.3] §5.3 (Downstream Correlation Analysis): The stronger predictive power of SC scores versus ImageNet accuracy is presented without details on pre-specification of category subsets, aggregation method across the 100 categories, or controls for category difficulty. Post-hoc selection could inflate the reported advantage; a concrete test (e.g., fixed held-out category split) is needed to support the claim.

    Authors: We acknowledge that the original manuscript lacked explicit details on category subset selection, aggregation procedures, and controls for category difficulty, which could raise concerns about post-hoc analysis. The subsets were chosen based on the intersection of categories with available downstream annotations, and correlations were aggregated via mean Pearson coefficients across categories. To address this rigorously, the revision will document the exact aggregation method, include controls for category difficulty (e.g., via regression with difficulty proxies), and report results from a pre-specified held-out split: a predictive model trained on 70 categories and evaluated on the remaining 30 to verify that SC scores retain stronger predictive power than ImageNet accuracy. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark with independent measurements

full rationale

The paper introduces a new benchmark (SOCO) with keypoint annotations and reports empirical correlations between SC scores and downstream task performance. No derivation chain, equations, fitted parameters, or self-citation load-bearing steps exist that reduce any claimed result to its inputs by construction. The reported correlations are computed from separate evaluations on external tasks and models, rendering the analysis self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper introduces a benchmark rather than a derivation; no free parameters, axioms, or invented entities are described in the abstract.

pith-pipeline@v0.9.1-grok · 5780 in / 1208 out tokens · 24127 ms · 2026-06-28T23:00:54.702137+00:00 · methodology

discussion (0)

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Works this paper leans on

82 extracted references · 12 linked inside Pith

  1. [1]

    NeurIPS35(2022)

    Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millicah, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., Simonyan, K.: Flamingo: A...

    2022

  2. [2]

    In: CVPR

    Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D human pose estimation: New benchmark and state of the art analysis. In: CVPR. pp. 3686–3693 (2014)

    2014

  3. [3]

    In: CVPR

    Assran, M., Duval, Q., Misra, I., Bojanowski, P., Vincent, P., Rabbat, M., Le- Cun, Y., Ballas, N.: Self-supervised learning from images with a joint-embedding predictive architecture. In: CVPR. pp. 15619–15629 (2023)

    2023

  4. [4]

    Aydemir, G., Xie, W., Güney, F.: Can visual foundation models achieve long-term point tracking? In: ECCV Workshops (2024)

    2024

  5. [5]

    arXiv preprint arXiv:2308.12966 (2023)

    Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., Zhou, J.: Qwen-VL: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966 (2023)

    Pith/arXiv arXiv 2023

  6. [6]

    arXiv preprint arXiv:2511.21631 (2025)

    Bai, S., Cai, Y., Chen, R., Chen, K., Chen, X., Cheng, Z., Deng, L., Ding, W., Gao, C., Ge, C., et al.: Qwen3-VL technical report. arXiv preprint arXiv:2511.21631 (2025)

    Pith/arXiv arXiv 2025

  7. [7]

    arXiv preprint arXiv:2502.13923 (2025)

    Bai, S., Chen, K., Liu, X., Wang, J., Ge, W., Song, S., Dang, K., Wang, P., Wang, S., Tang, J., Zhong, H., Zhu, Y., Yang, M., Li, Z., Wan, J., Wang, P., Ding, W., Fu, Z., Xu, Y., Ye, J., Zhang, X., Xie, T., Cheng, Z., Zhang, H., Yang, Z., Xu, H., Lin, J.: Qwen2.5-VL technical report. arXiv preprint arXiv:2502.13923 (2025)

    Pith/arXiv arXiv 2025

  8. [8]

    arXiv preprint arXiv:2111.08897 (2021)

    Baruch, G., Chen, Z., Dehghan, A., Dimry, T., Feigin, Y., Fu, P., Gebauer, T., Joffe, B., Kurz, D., Schwartz, A., Shulman, E.: ARKitScenes: A diverse real- world dataset for 3D indoor scene understanding using mobile RGB-D data. arXiv preprint arXiv:2111.08897 (2021)

    Pith/arXiv arXiv 2021

  9. [9]

    arXiv preprint arXiv:2504.13181 (2025)

    Bolya, D., Huang, P.Y., Sun, P., Cho, J.H., Madotto, A., Wei, C., Ma, T., Zhi, J., Rajasegaran, J., Rasheed, H., et al.: Perception encoder: The best visual em- beddings are not at the output of the network. arXiv preprint arXiv:2504.13181 (2025)

    Pith/arXiv arXiv 2025

  10. [10]

    In: CVPR (2023)

    Brazil, G., Kumar, A., Straub, J., Ravi, N., Johnson, J., Gkioxari, G.: Omni3D: A large benchmark and model for 3D object detection in the wild. In: CVPR (2023)

    2023

  11. [11]

    In: ECCV

    Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: ECCV. pp. 611–625 (2012)

    2012

  12. [12]

    arXiv preprint arXiv:2509.25413 (2025)

    Cai, Z., Yeh, C.F., Xu, H., Liu, Z., Meyer, G., Lei, X., Zhao, C., Li, S.W., Chandra, V., Shi, Y.: DepthLM: Metric depth from vision language models. arXiv preprint arXiv:2509.25413 (2025)

    arXiv 2025

  13. [13]

    In: ICCV (2021)

    Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV (2021)

    2021

  14. [14]

    In: 3DV (2026)

    Chi, Y., Sommer, L., Dünkel, O., Muhle, D., Cremers, D., Theobalt, C., Ko- rtylewski, A.: C3PO: Canonicalization of 3D pose from partial views with gen- eralizable correspondence features. In: 3DV (2026)

    2026

  15. [15]

    In: CVPR

    Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: CVPR. pp. 3213–3223 (2016)

    2016

  16. [16]

    In: CVPR

    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: A large-scale hierarchical image database. In: CVPR. pp. 248–255 (2009) 20 O. Dünkel et al

    2009

  17. [17]

    NeurIPS35, 13610–13626 (2022)

    Doersch,C.,Gupta,A.,Markeeva,L.,Recasens,A.,Smaira,L.,Aytar,Y.,Carreira, J., Zisserman, A., Yang, Y.: TAP-Vid: A benchmark for tracking any point in a video. NeurIPS35, 13610–13626 (2022)

    2022

  18. [18]

    In: ICCV (2025)

    Dünkel, O., Jesslen, A., Xie, J., Theobalt, C., Rupprecht, C., Kortylewski, A.: CNS-Bench: Benchmarking image classifier robustness under continuous nuisance shifts. In: ICCV (2025)

    2025

  19. [19]

    In: ICCV (2025)

    Dünkel, O., Wimmer, T., Theobalt, C., Rupprecht, C., Kortylewski, A.: Do it yourself: Learning semantic correspondence from pseudo-labels. In: ICCV (2025)

    2025

  20. [20]

    In: CVPR

    El Banani, M., Raj, A., Maninis, K.K., Kar, A., Li, Y., Rubinstein, M., Sun, D., Guibas, L., Johnson, J., Jampani, V.: Probing the 3D awareness of visual foundation models. In: CVPR. pp. 21795–21806 (2024)

    2024

  21. [21]

    IJCV 111(1), 98–136 (2015)

    Everingham, M., Eslami, S.M.A., Van Gool, L., Williams, C.K.I., Winn, J., Zis- serman, A.: The PASCAL visual object classes challenge: A retrospective. IJCV 111(1), 98–136 (2015)

    2015

  22. [22]

    IJCV88(2), 303–338 (2010)

    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes (VOC) challenge. IJCV88(2), 303–338 (2010)

    2010

  23. [23]

    arXiv preprint arXiv:1806.08756 (2018)

    Florence, P.R., Manuelli, L., Tedrake, R.: Dense object nets: Learning dense visual object descriptors by and for robotic manipulation. arXiv preprint arXiv:1806.08756 (2018)

    Pith/arXiv arXiv 2018

  24. [24]

    In: ECCV (2024)

    Fu, X., Hu, Y., Li, B., Feng, Y., Wang, H., Lin, X., Roth, D., Smith, N.A., Ma, W.C., Krishna, R.: BLINK: Multimodal large language models can see but not perceive. In: ECCV (2024)

    2024

  25. [25]

    In: NeurIPS (2025)

    Gan, C., Tu, Y., Chen, X., Chen, T., Li, Y., Harandi, M., Lin, W.: Unleashing diffusiontransformersforvisualcorrespondencebymodulatingmassiveactivations. In: NeurIPS (2025)

    2025

  26. [26]

    arXiv preprint arXiv:2312.11805 (2023)

    Gemini Team: Gemini: A family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)

    Pith/arXiv arXiv 2023

  27. [27]

    In: CVPR

    Ham, B., Cho, M., Schmid, C., Ponce, J.: Proposal flow. In: CVPR. pp. 3475–3484 (2016)

    2016

  28. [28]

    In: CVPR

    He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: CVPR. pp. 16000–16009 (2022)

    2022

  29. [29]

    In: CVPR (2025)

    Heinrich, G., Ranzinger, M., Yin, H., Lu, Y., Kautz, J., Tao, A., Catanzaro, B., Molchanov, P.: RADIOv2.5: Improved baselines for agglomerative vision founda- tion models. In: CVPR (2025)

    2025

  30. [30]

    arXiv preprint arXiv:2410.21276 (2024)

    Hurst, A., Lerer, A., Goucher, A.P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A.J., Welihinda, A., Hayes, A., Radford, A., et al.: GPT-4o system card. arXiv preprint arXiv:2410.21276 (2024)

    Pith/arXiv arXiv 2024

  31. [31]

    In: NeurIPS (2023)

    Jampani, V., Maninis, K.K., Engelhardt, A., Karpur, A., Truong, K., Sargent, K., Popov, S., Araujo, A., Martin-Brualla, R., Patel, K., Vlasic, D., Ferrari, V., Makadia, A., Liu, C., Li, Y., Zhou, H.: NAVI: Category-agnostic image collections with high-quality 3D shape and pose annotations. In: NeurIPS (2023)

    2023

  32. [32]

    Kornblith, S., Shlens, J., Le, Q.V.: Do better ImageNet models transfer better? In: CVPR. pp. 2661–2671 (2019)

    2019

  33. [33]

    TMLR (2024)

    Li, B., Zhang, Y., Guo, D., Zhang, R., Li, F., Zhang, H., Zhang, K., Zhang, P., Li, Y., Liu, Z., Li, C.: LLaVA-OneVision: Easy visual task transfer. TMLR (2024)

    2024

  34. [34]

    In: ICML (2022)

    Li, J., Li, D., Xiong, C., Hoi, S.: BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: ICML (2022)

    2022

  35. [35]

    In: CVPR

    Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR. pp. 936–944 (2017) SOCO 21

    2017

  36. [36]

    In: ECCV

    Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: Common objects in context. In: ECCV. pp. 740–755 (2014)

    2014

  37. [37]

    In: ECCV

    Liu, C., Yuen, J., Torralba, A., Sivic, J., Freeman, W.T.: SIFT flow: Dense corre- spondence across different scenes. In: ECCV. pp. 28–42 (2008)

    2008

  38. [38]

    In: NeurIPS (2023)

    Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. In: NeurIPS (2023)

    2023

  39. [39]

    Liu, Y., Duan, H., Zhang, Y., Li, B., Zhang, S., Zhao, W., Yuan, Y., Wang, J., He, C., Liu, Z., et al.: MMBench: Is your multi-modal model an all-around player? In: ECCV. pp. 216–233 (2024)

    2024

  40. [40]

    In: NeurIPS (2023)

    Luo, G., Dunlap, L., Park, D.H., Holynski, A., Darrell, T.: Diffusion hyperfeatures: Searching through time and space for semantic correspondence. In: NeurIPS (2023)

    2023

  41. [41]

    NeurIPS37, 96127–96149 (2024)

    Ma, W., Zhang, G., Liu, Q., Zeng, G., Kortylewski, A., Liu, Y., Yuille, A.: Im- ageNet3D: Towards general-purpose object-level 3D understanding. NeurIPS37, 96127–96149 (2024)

    2024

  42. [42]

    arXiv preprint arXiv:2506.08220 (2025)

    Mariotti, O., Du, Z., Bhalgat, Y., Mac Aodha, O., Bilen, H.: Jamais vu: Expos- ing the generalization gap in supervised semantic correspondence. arXiv preprint arXiv:2506.08220 (2025)

    arXiv 2025

  43. [43]

    In: CVPR

    Mariotti, O., Mac Aodha, O., Bilen, H.: Improving semantic correspondence with viewpoint-guided spherical maps. In: CVPR. pp. 19521–19530 (2024)

    2024

  44. [44]

    In: CVPR

    Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: CVPR. pp. 4040–4048 (2016)

    2016

  45. [45]

    arXiv preprint arXiv:1908.10543 (2019)

    Min, J., Lee, J., Ponce, J., Cho, M.: SPair-71k: A large-scale benchmark for se- mantic correspondence. arXiv preprint arXiv:1908.10543 (2019)

    arXiv 1908

  46. [46]

    OpenAI, Applin, S., Adesso, G., Ashfaq, R., Bai, M., Brammer, M., Fecht, E., Goodman, A., Grossman, S., Groh, M., Kirk, H.R., Gunitsky, S., Huang, Y., Kahn, L., Kumar, S., Madrid-Morales, D., Motoki, F., Ovadya, A., Peters, U., Robinson, M., Röttger, P., Wasserman, H., Wehsener, A., Walker, L., Vidgen, B., Zhu, J.: GPT-4V(ision) system card. Tech. rep., O...

    2023

  47. [47]

    TMLR (2024)

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H.V., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Howes, R., Huang, P.Y., Xu, H., Sharma, V., Li, S.W., Galuba, W., Rabbat, M., Assran, M., Ballas, N., Syn- naeve, G., Misra, I., Jégou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P.: DINOv2: Learning robust visual fe...

    2024

  48. [48]

    In: ICML

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

    2021

  49. [49]

    In: ICCV

    Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: ICCV. pp. 12179–12188 (2021)

    2021

  50. [50]

    In: CVPR

    Ranzinger,M.,Heinrich,G.,Kautz,J.,Molchanov,P.:AM-RADIO:Agglomerative vision foundation model reduce all domains into one. In: CVPR. pp. 12490–12500 (2024)

    2024

  51. [51]

    arXiv preprint arXiv:2601.17237 (2026)

    Ranzinger, M., Heinrich, G., McCarthy, C., Kautz, J., Tao, A., Catanzaro, B., Molchanov, P.: C-RADIOv4 technical report. arXiv preprint arXiv:2601.17237 (2026)

    arXiv 2026

  52. [52]

    In: CVPR

    Rocco, I., Arandjelovic, R., Sivic, J.: Convolutional neural network architecture for geometric matching. In: CVPR. pp. 6148–6157 (2017)

    2017

  53. [53]

    In: CVPR

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR. pp. 10684–10695 (2022) 22 O. Dünkel et al

    2022

  54. [54]

    In: CVPR

    Sarıyıldız, M.B., Weinzaepfel, P., Lucas, T., De Jorge, P., Larlus, D., Kalantidis, Y.: DUNE: Distilling a universal encoder from heterogeneous 2D and 3D teachers. In: CVPR. pp. 30084–30094 (2025)

    2025

  55. [55]

    IJCV47(1), 7–42 (2002)

    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV47(1), 7–42 (2002)

    2002

  56. [56]

    In: ICCV (2015)

    Sedaghat, N., Brox, T.: Unsupervised generation of a viewpoint annotated car dataset from videos. In: ICCV (2015)

    2015

  57. [57]

    Shen, S., Li, L.H., Tan, H., Bansal, M., Rohrbach, A., Chang, K.W., Yao, Z., Keutzer, K.: How much can CLIP benefit vision-and-language tasks? In: ICLR (2022)

    2022

  58. [58]

    In: ICCV (2023)

    Shtedritski, A., Rupprecht, C., Vedaldi, A.: What does CLIP know about a red circle? visual prompt engineering for VLMs. In: ICCV (2023)

    2023

  59. [59]

    In: ECCV (2012)

    Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGB-D images. In: ECCV (2012)

    2012

  60. [60]

    arXiv preprint arXiv:2508.10104 (2025)

    Siméoni, O., Vo, H.V., Seitzer, M., Baldassarre, F., Oquab, M., Jose, C., Khalidov, V., Szafraniec, M., Yi, S., Ramamonjisoa, M., Massa, F., Haziza, D., Wehrstedt, L., Wang, J., Darcet, T., Moutakanni, T., Sentana, L., Roberts, C., Vedaldi, A., Tolan, J., Brandt, J., Couprie, C., Mairal, J., Jégou, H., Labatut, P., Bojanowski, P.: DINOv3. arXiv preprint a...

    Pith/arXiv arXiv 2025

  61. [61]

    In: CVPR

    Sommer, L., Dünkel, O., Theobalt, C., Kortylewski, A.: Common3D: Self- supervised learning of 3D morphable models for common objects in neural feature space. In: CVPR. pp. 6468–6479 (2025)

    2025

  62. [62]

    In: CVPR

    Stracke, N., Baumann, S.A., Bauer, K., Fundel, F., Ommer, B.: CleanDIFT: Dif- fusion features without noise. In: CVPR. pp. 117–127 (2025)

    2025

  63. [63]

    In: CVPR

    Sun, Y., Huang, Y., Guo, H., Zhao, Y., Wu, R., Yu, Y., Ge, W., Zhang, W.: MISC210K: A large-scale dataset for multi-instance semantic correspondence. In: CVPR. pp. 7121–7130 (2023)

    2023

  64. [64]

    NeurIPS36, 1363–1389 (2023)

    Tang, L., Jia, M., Wang, Q., Phoo, C.P., Hariharan, B.: Emergent correspondence from image diffusion. NeurIPS36, 1363–1389 (2023)

    2023

  65. [65]

    In: CVPR

    Taniai, T., Sinha, S.N., Sato, Y.: Joint recovery of dense correspondence and coseg- mentation in two images. In: CVPR. pp. 4246–4255 (2016)

    2016

  66. [66]

    arXiv preprint arXiv:2507.14137 (2025)

    Venkataramanan, S., Pariza, V., Salehi, M., Knobel, L., Gidaris, S., Ramzi, E., Bursuc, A., Asano, Y.M.: Franca: Nested matryoshka clustering for scalable visual representation learning. arXiv preprint arXiv:2507.14137 (2025)

    Pith/arXiv arXiv 2025

  67. [67]

    In: CVPR (2025)

    Wandel, K., Wang, H.: SemAlign3D: Semantic correspondence between RGB im- ages through aligning 3D object-class representations. In: CVPR (2025)

    2025

  68. [68]

    In: CVPR

    Wang, H., Sridhar, S., Huang, J., Valentin, J., Song, S., Guibas, L.J.: Normalized object coordinate space for category-level 6D object pose and size estimation. In: CVPR. pp. 2642–2651 (2019)

    2019

  69. [69]

    arXiv preprint arXiv:2508.18265 (2025)

    Wang, W., Gao, Z., Gu, L., Pu, H., Cui, L., Wei, X., Liu, Z., Jing, L., Ye, S., Shao, J., et al.: InternVL3.5: Advancing open-source multimodal models in versatility, reasoning, and efficiency. arXiv preprint arXiv:2508.18265 (2025)

    Pith/arXiv arXiv 2025

  70. [70]

    In: ICCV (2023)

    Weinzaepfel, P., Lucas, T., Leroy, V., Cabon, Y., Arora, V., Brégier, R., Csurka, G., Antsfeld, L., Chidlovskii, B., Revaud, J.: CroCo v2: Improved cross-view com- pletion pre-training for stereo matching and optical flow. In: ICCV (2023)

    2023

  71. [71]

    In: CVPR

    Wu, Y., Lim, J., Yang, M.H.: Online object tracking: A benchmark. In: CVPR. pp. 2411–2418 (2013)

    2013

  72. [72]

    Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2 (2019), software

    2019

  73. [73]

    In: WACV

    Xiang, Y., Mottaghi, R., Savarese, S.: Beyond PASCAL: A benchmark for 3D object detection in the wild. In: WACV. pp. 75–82 (2014) SOCO 23

    2014

  74. [74]

    In: ICCV

    Xu, J., Zhang, Y., Peng, J., Ma, W., Jesslen, A., Ji, P., Hu, Q., Zhang, J., Liu, Q., Wang, J., et al.: Animal3D: A comprehensive dataset of 3D animal pose and shape. In: ICCV. pp. 9099–9109 (2023)

    2023

  75. [75]

    arXiv preprint arXiv:2412.14171 (2024)

    Yang, J., Yang, S., Gupta, A.W., Han, R., Fei-Fei, L., Xie, S.: Thinking in space: How multimodal large language models see, remember and recall spaces. arXiv preprint arXiv:2412.14171 (2024)

    Pith/arXiv arXiv 2024

  76. [76]

    arXiv preprint arXiv:2512.15715 (2025)

    Yang, L., Li, S.W., Li, Y., Lei, X., Wang, D., Mohamed, A., Zhao, H., Xu, H.: In pursuit of pixel supervision for visual pre-training. arXiv preprint arXiv:2512.15715 (2025)

    arXiv 2025

  77. [77]

    arXiv preprint arXiv:2108.12617 (2021)

    Yu, H., Xu, Y., Zhang, J., Zhao, W., Guan, Z., Tao, D.: AP-10K: A benchmark for animal pose estimation in the wild. arXiv preprint arXiv:2108.12617 (2021)

    arXiv 2021

  78. [78]

    In: CVPR (2024)

    Yue, X., Ni, Y., Zhang, K., Zheng, T., Liu, R., Zhang, G., Stevens, S., Jiang, D., Ren, W., Sun, Y., Wei, C., Yu, B., Yuan, R., Sun, R., Yin, M., Zheng, B., Yang, Z., Liu, Y., Huang, W., Sun, H., Su, Y., Chen, W.: MMMU: A massive multi-discipline multimodal understanding and reasoning benchmark for expert AGI. In: CVPR (2024)

    2024

  79. [79]

    In: CVPR

    Zhang, J., Herrmann, C., Hur, J., Chen, E., Jampani, V., Sun, D., Yang, M.H.: Telling left from right: Identifying geometry-aware semantic correspondence. In: CVPR. pp. 3076–3085 (2024)

    2024

  80. [80]

    NeurIPS36, 45533–45547 (2023)

    Zhang, J., Herrmann, C., Hur, J., Polania Cabrera, L., Jampani, V., Sun, D., Yang, M.H.: A tale of two features: Stable Diffusion complements DINO for zero-shot semantic correspondence. NeurIPS36, 45533–45547 (2023)

    2023

Showing first 80 references.