Pith. sign in

REVIEW 2 major objections 2 minor 119 references

Peel neighborhoods give a canonical, parameter-free local neighborhood in finite metric spaces of strict negative type.

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

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

T0 review · grok-4.5

2026-07-14 20:06 UTC pith:NI6SDO5A

load-bearing objection Only the abstract of Peel neighborhoods is available; the supplied full text is a mismatched CV paper, so the math cannot be audited—but the claimed construction is a clean, classical-hypothesis idea that would be useful if the body delivers. the 2 major comments →

arxiv 2603.26645 v2 pith:NI6SDO5A submitted 2026-03-27 math.MG math.AT

Peel neighborhoods

Steve Huntsman This is my paper
classification math.MG math.AT MSC 51F9954E3562H30
keywords peel neighborhoodsstrict negative typefinite metric spaceslocal dimensionsingularitiesstratified manifoldsmetric geometry
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper defines peel neighborhoods as a canonical way to pick local neighborhoods in a finite metric space that has strict negative type. The construction needs no free parameters, can be computed efficiently, and scales when a soft upper bound is put on radius or size. That scaling makes peel neighborhoods practical for describing geometry and topology at a fine scale. As a concrete use, they support fast local-dimension estimates and singularity detection on point samples from stratified manifolds. A sympathetic reader cares because many data sets are finite metric spaces, and a parameter-free local neighborhood is a basic building block for geometric and topological analysis of those spaces.

Core claim

In a finite metric space of strict negative type there exists a canonical, parameter-free, efficiently computable notion of local neighborhood—the peel neighborhood—and, once a soft threshold bounds its radius or cardinality, these neighborhoods can be computed at scale and used for microscopic geometric and topological descriptions, including local-dimension estimation and singularity detection on stratified-manifold samples.

What carries the argument

Peel neighborhoods: a canonical, parameter-free local neighborhood construction in finite metric spaces of strict negative type, made scalable by a soft threshold on radius or cardinality.

Load-bearing premise

The finite metric space must be of strict negative type; if real data metrics fail that classical condition, the claimed canonicity and algorithms may not apply as stated.

What would settle it

Take a finite metric space known not to be of strict negative type, or a stratified-manifold sample whose metric fails the condition, and check whether peel neighborhoods remain well-defined, unique, efficiently computable, and still recover local dimension and singularities as claimed.

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

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The abstract claims to introduce peel neighborhoods: a canonical, parameter-free, efficiently computable local neighborhood construction on finite metric spaces of strict negative type, optionally capped by a soft radius/cardinality threshold for scalability, with applications to local-dimension estimation and singularity detection on samples from stratified manifolds. The supplied full manuscript body, however, is an entirely different paper (EgoPoint-Ground / SV-CoT on egocentric hand-pointing visual grounding, arXiv:2603.26646, cs.CV). No definitions, theorems, algorithms, complexity statements, or experiments for peel neighborhoods appear in the body.

Significance. If the abstract’s claims hold for a correctly supplied math.MG manuscript, a canonical parameter-free neighborhood notion with scalable computation and concrete geometric applications would be of genuine interest in metric geometry and geometric data analysis. As submitted, that significance cannot be assessed: the body contains no mathematical content matching the title or abstract, so no credit can be given for proofs, complexity bounds, or empirical performance.

major comments (2)
  1. Title/abstract vs. body mismatch: paper_id 2603.26645 and the abstract concern peel neighborhoods in finite metric spaces of strict negative type, but the full manuscript text is the unrelated EgoPoint-Ground CV paper (hand-pointing referring expressions, SV-CoT, Tables 1–4, etc.). No definition of peel neighborhood, no negative-type hypothesis, no algorithm, and no local-dimension/singularity results are present. The central claims are therefore unverifiable from the submission as provided.
  2. Because the mathematical body is absent, load-bearing claims in the abstract—canonicity, parameter-freeness (beyond the acknowledged soft threshold), efficient computability, and utility for local dimension and singularity detection—cannot be checked against definitions, proofs, or experiments. A referee report on the actual math.MG contribution is not possible until the correct manuscript is supplied.
minor comments (2)
  1. The abstract alone is coherent on its face and conditions the construction on the classical hypothesis of strict negative type; residual circularity risk from the soft threshold is low as stated. These points cannot be elevated to major technical objections without the correct body.
  2. If the wrong full text was attached in error, the authors should resubmit the actual peel-neighborhoods manuscript; the present package is not reviewable as a math.MG paper.

Circularity Check

0 steps flagged

No circularity detectable: only the abstract of Peel neighborhoods is available; the supplied full text is a mismatched CV paper.

full rationale

The target paper (arXiv:2603.26645, Peel neighborhoods) is represented only by its abstract. That abstract defines peel neighborhoods as a canonical, parameter-free construction on finite metric spaces of strict negative type, with an optional soft threshold used solely as a computational cap on radius or cardinality, not as a fitted parameter that defines the neighborhoods or the quantities they estimate. Local-dimension estimates and singularity detection are presented as downstream applications, not as inputs to the definition. No equations, self-citations, uniqueness theorems, or fitted-then-predicted quantities appear in the available text, so no reduction of a claimed prediction to its own inputs can be exhibited. The CACHEABLE full manuscript is a completely different paper (EgoPoint-Ground / SV-CoT, arXiv:2603.26646); it cannot be used to audit the derivation chain of 2603.26645. Under the abstract alone the construction is self-contained and non-circular; residual risk that the missing mathematical body hides definitional circularity cannot be audited and is not scored as circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

Abstract-only review of peel neighborhoods. Load-bearing background is the classical metric condition of strict negative type and the existence of a well-defined finite metric on the sample. Soft thresholds are optional computational parameters, not definitional free parameters of the neighborhood itself. No invented physical entities appear. Full axiom inventory would require the missing mathematical body.

free parameters (1)
  • soft threshold on radius or cardinality
    Abstract states a soft threshold is used to upper-bound radius or cardinality for scalability; if this threshold is chosen by hand or tuned to data, it is a free computational parameter even if the ideal peel neighborhood is parameter-free.
axioms (2)
  • domain assumption The finite metric space is of strict negative type.
    The entire construction is stated only for spaces of strict negative type; this is a classical metric hypothesis, not proved in the abstract.
  • domain assumption Samples are drawn from stratified manifolds (for the application).
    Local-dimension and singularity claims are illustrated on stratified-manifold samples; the abstract assumes that modeling regime.
invented entities (1)
  • peel neighborhood no independent evidence
    purpose: Canonical local neighborhood for microscopic geometric/topological description in strict-negative-type finite metric spaces.
    Primary new object of the paper; independent evidence would be theorems, algorithms, and experiments in the body, which are not available here.

pith-pipeline@v1.1.0-grok45 · 29705 in / 2279 out tokens · 26413 ms · 2026-07-14T20:06:37.571863+00:00 · methodology

0 comments
read the original abstract

We introduce the canonical, parameter-free, and efficiently computable notion of peel neighborhoods in a finite metric space of strict negative type. Using a soft threshold to upper bound their radius or cardinality allows peel neighborhoods to be computed at scale, enabling useful microscopic descriptions of geometry and topology. As an example of their utility, peel neighborhoods enable efficient and performant estimates of local dimension and detections of singularities in samples from stratified manifolds.

discussion (0)

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

Reference graph

Works this paper leans on

119 extracted references · 25 linked inside Pith

  1. [1]

    ArXiv Preprint ArXiv:2303.08774 (2023)

    Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: GPT-4 Technical Report. ArXiv Preprint ArXiv:2303.08774 (2023)

    Pith/arXiv arXiv 2023

  2. [2]

    Ahuja, C.: Communication Beyond Words: Grounding Visual Body Motion with Language. Ph.D. thesis, Carnegie Mellon University (2022)

    2022

  3. [3]

    ArXiv Preprint ArXiv:2509.23661 (2025)

    An, X., Xie, Y., Yang, K., Zhang, W., Zhao, X., Cheng, Z., Wang, Y., Xu, S., Chen, C., Wu, C., et al.: Llava-onevision-1.5: Fully Open Framework for Democratized Multimodal Training. ArXiv Preprint ArXiv:2509.23661 (2025)

    Pith/arXiv arXiv 2025

  4. [4]

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

    Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sünderhauf, N., Reid, I., Gould, S., Van Den Hengel, A.: Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3674– 3683 (2018)

    2018

  5. [5]

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

    Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2D Human Pose Estimation: New Benchmark and State of the Art Analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3686–3693 (2014)

    2014

  6. [6]

    In: Proceedings of the IEEE International Conference on Computer Vision

    Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: VQA: Visual Question Answering. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2425–2433 (2015)

    2015

  7. [7]

    Computers11, 28 (2022)

    Arena, F., Collotta, M., Pau, G., Termine, F.: An Overview of Augmented Reality. Computers11, 28 (2022)

    2022

  8. [8]

    ArXiv Preprint ArXiv:2309.16609 (2023)

    Bai, J., Bai, S., Chu, Y., Cui, Z., Dang, K., Deng, X., Fan, Y., Ge, W., Han, Y., Huang, F., et al.: Qwen Technical Report. ArXiv Preprint ArXiv:2309.16609 (2023)

    Pith/arXiv arXiv 2023

  9. [9]

    In: Proceedings of the European Conference on Computer Vision

    Bansal, A., Sikka, K., Sharma, G., Chellappa, R., Divakaran, A.: Zero-Shot Object Detection. In: Proceedings of the European Conference on Computer Vision. pp. 384–400 (2018)

    2018

  10. [10]

    In: Proceedings of the European Conference on Computer Vision

    Bansal, S., Arora, C., Jawahar, C.: My View is the Best View: Procedure Learning from Egocentric Videos. In: Proceedings of the European Conference on Computer Vision. pp. 657–675 (2022)

    2022

  11. [11]

    In: Healthcare

    Banyai, A.D., Bris�an, C.: Robotics in Physical Rehabilitation: Systematic Review. In: Healthcare. vol. 12, p. 1720 (2024)

    2024

  12. [12]

    In: European Conference on Computer Vision

    Bhattacharyya, P., Mitton, J., Page, R., Morgan, O., Menzies, B., Homewood, G., Jacobs, K., Baesso, P., Trickett, D., Mair, C., et al.: Helios: An extremely low power event-based gesture recognition for always-on smart eyewear. In: European Conference on Computer Vision. pp. 168–184. Springer (2024)

    2024

  13. [13]

    Handbook of aug- mented reality pp

    Carmigniani, J., Furht, B.: Augmented Reality: An Overview. Handbook of aug- mented reality pp. 3–46 (2011)

    2011

  14. [14]

    International Journal of Computer Applications120(2015)

    Chandel, H., Vatta, S.: Occlusion Detection and Handling: A Review. International Journal of Computer Applications120(2015)

    2015

  15. [15]

    ArXiv Preprint ArXiv:2410.21091 (2024)

    Chen, J., Grubert, J., Kristensson, P.O.: Large Language Model-assisted Speech and Pointing Benefits Multiple 3D Object Selection in Virtual Reality. ArXiv Preprint ArXiv:2410.21091 (2024)

    Pith/arXiv arXiv 2024

  16. [16]

    ArXiv Preprint ArXiv:2306.15195 (2023)

    Chen, K., Zhang, Z., Zeng, W., Zhang, R., Zhu, F., Zhao, R.: Shikra: Unleashing Multimodal LLM’s Referential Dialogue Magic. ArXiv Preprint ArXiv:2306.15195 (2023)

    Pith/arXiv arXiv 2023

  17. [17]

    In: European Conference on Computer Vision

    Chen, W., Chen, L., Wu, Y.: An Efficient and Effective Transformer Decoder- Based Framework for Multi-Task Visual Grounding. In: European Conference on Computer Vision. pp. 125–141. Springer (2024) Abbreviated paper title 17

    2024

  18. [18]

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

    Chen, Y., Li, Q., Kong, D., Kei, Y.L., Zhu, S.C., Gao, T., Zhu, Y., Huang, S.: YouRefIt: Embodied Reference Understanding With Language and Gesture. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1385–1395 (2021)

    2021

  19. [19]

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

    Chen, Z., Wang, P., Ma, L., Wong, K.Y.K., Wu, Q.: Cops-Ref: A New Dataset and Task on Compositional Referring Expression Comprehension. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10086–10095 (2020)

    2020

  20. [20]

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

    Cheng, T., Song, L., Ge, Y., Liu, W., Wang, X., Shan, Y.: YOLO-World: Real-Time Open-Vocabulary Object Detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16901–16911 (2024)

    2024

  21. [21]

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing17, 19640–19667 (2024)

    Chu, Y., Ye, M., Qian, Y.: Fine-Grained Image Recognition Methods and Their Applications in Remote Sensing Images: A Review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing17, 19640–19667 (2024)

    2024

  22. [22]

    ArXiv Preprint ArXiv:2507.22062 (2025)

    Chuang, Y.S., Li, Y., Wang, D., Yeh, C.F., Lyu, K., Raghavendra, R., Glass, J., Huang, L., Weston, J., Zettlemoyer, L., et al.: Meta CLIP 2: A Worldwide Scaling Recipe. ArXiv Preprint ArXiv:2507.22062 (2025)

    Pith/arXiv arXiv 2025

  23. [23]

    In: European conference on computer vision

    Constantin, S., Eyiokur, F.I., Yaman, D., Bärmann, L., Waibel, A.: Interactive Multimodal Robot Dialog Using Pointing Gesture Recognition. In: European conference on computer vision. pp. 640–657. Springer (2022)

    2022

  24. [24]

    In: Proceedings of the Advances in Neural Information Processing Systems

    Dai, M., Yang, L., Xu, Y., Feng, Z., Yang, W.: SimVG: A Simple Framework for Visual Grounding with Decoupled Multi-modal Fusion. In: Proceedings of the Advances in Neural Information Processing Systems. pp. 121670–121698 (2024)

    2024

  25. [25]

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

    De Vries, H., Strub, F., Chandar, S., Pietquin, O., Larochelle, H., Courville, A.: GuessWhat?! Visual Object Discovery Through Multi-Modal Dialogue. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5503–5512 (2017)

    2017

  26. [26]

    arXiv preprint arXiv:2410.00253 (2024)

    Deichler, A., O’Regan, J., Beskow, J.: MM-Conv: A Multi-modal Conversational Dataset for Virtual Humans. arXiv preprint arXiv:2410.00253 (2024)

    Pith/arXiv arXiv 2024

  27. [27]

    In: European Conference on Computer Vision

    Delmas, G., Weinzaepfel, P., Moreno-Noguer, F., Rogez, G.: PoseEmbroider: Towards a 3D, Visual, Semantic-aware Human Pose Representation. In: European Conference on Computer Vision. pp. 55–73. Springer (2024)

    2024

  28. [28]

    IEEE Transactions on Emerging Topics in Compu- tational Intelligence6, 230–244 (2022)

    Duan, J., Yu, S., Tan, H.L., Zhu, H., Tan, C.: A Survey of Embodied AI: From Simulators to Research Tasks. IEEE Transactions on Emerging Topics in Compu- tational Intelligence6, 230–244 (2022)

    2022

  29. [29]

    Efron, R.: What is Perception? In: Proceedings of the Boston Colloquium for the Philosophy of Science 1966/1968. pp. 137–173 (1969)

    1966

  30. [30]

    In: European Conference on Computer Vision

    Fan, Z., Ohkawa, T., Yang, L., Lin, N., Zhou, Z., Zhou, S., Liang, J., Gao, Z., Zhang, X., Zhang, X., et al.: Benchmarks and Challenges in Pose Estimation for Egocentric Hand Interactions with Objects. In: European Conference on Computer Vision. pp. 428–448. Springer (2024)

    2024

  31. [31]

    In: European Conference on Computer Vision

    Farkhondeh, A., Tafasca, S., Odobez, J.M.: ChildPlay-Hand: A Dataset of Hand Manipulations in the Wild. In: European Conference on Computer Vision. pp. 101–118. Springer (2024)

    2024

  32. [32]

    Studia Linguistica78(1), 1–7 (2024)

    Fortuny, J., Payrató, L.: Ambiguity in Linguistics. Studia Linguistica78(1), 1–7 (2024)

    2024

  33. [33]

    arXiv preprint arXiv:2602.17573 (2026) 18 L

    Foteinos, K., Angelidis, G., Psiris, A., Argyriou, V., Sarigiannidis, P., Papadopou- los, G.T.: FR-GESTURE: An RGBD Dataset For Gesture-based Human-Robot Interaction In First Responder Operations. arXiv preprint arXiv:2602.17573 (2026) 18 L. Li et al

    arXiv 2026

  34. [34]

    ArXiv Preprint ArXiv:2504.11346 (2025)

    Gao, Y., Gong, L., Guo, Q., Hou, X., Lai, Z., Li, F., Li, L., Lian, X., Liao, C., Liu, L., et al.: Seedream 3.0 Technical Report. ArXiv Preprint ArXiv:2504.11346 (2025)

    Pith/arXiv arXiv 2025

  35. [35]

    IEEE Robotics & Automation Magazine14, 90–103 (2007)

    Garcia, E., Jimenez, M.A., De Santos, P.G., Armada, M.: The Evolution of Robotics Research. IEEE Robotics & Automation Magazine14, 90–103 (2007)

    2007

  36. [36]

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

    Grauman, K., Westbury, A., Byrne, E., Chavis, Z., Furnari, A., Girdhar, R., Hamburger, J., Jiang, H., Liu, M., Liu, X., et al.: Ego4D: Around the World in 3,000 Hours of Egocentric Video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 18995–19012 (2022)

    2022

  37. [37]

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

    Grauman, K., Westbury, A., Torresani, L., Kitani, K., Malik, J., Afouras, T., Ashutosh, K., Baiyya, V., Bansal, S., Boote, B., et al.: Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19383–19400 (2024)

    2024

  38. [38]

    5-vl Technical Report

    Guo, D., Wu, F., Zhu, F., Leng, F., Shi, G., Chen, H., Fan, H., Wang, J., Jiang, J., Wang, J., et al.: Seed1. 5-vl Technical Report. ArXiv Preprint ArXiv:2505.07062 (2025)

    Pith/arXiv arXiv 2025

  39. [39]

    ArXiv Preprint ArXiv:2401.17766 (2024)

    Guo, J., Rao, Z., Chen, Z., Zhou, J., Tao, D.: Fine-Grained Zero-Shot Learning: Advances, Challenges, and Prospects. ArXiv Preprint ArXiv:2401.17766 (2024)

    Pith/arXiv arXiv 2024

  40. [41]

    IEEE Transactions on Human-Machine Systems51, 300–309 (2021)

    Guo, L., Lu, Z., Yao, L.: Human-Machine Interaction Sensing Technology Based on Hand Gesture Recognition: A Review. IEEE Transactions on Human-Machine Systems51, 300–309 (2021)

    2021

  41. [42]

    IEEE Access12, 143599–143626 (2024)

    Hashi, A.O., Hashim, S.Z.M., Asamah, A.B.: A Systematic Review of Hand Gesture Recognition: An Update From 2018 to 2024. IEEE Access12, 143599–143626 (2024)

    2018

  42. [43]

    In: Proceedings of the IEEE International Conference on Computer Vision

    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2961–2969 (2017)

    2017

  43. [44]

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

    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

  44. [45]

    In: European Conference on Computer Vision

    Hong Da, N.L., Miura, J., Hayashi, K.: Enhancing Human-Robot Collaborative Search Through Efficient Space Sharing with On-Demand Bidirectional Interaction. In: European Conference on Computer Vision. pp. 20–35. Springer (2024)

    2024

  45. [46]

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

    Hu, R., Rohrbach, M., Andreas, J., Darrell, T., Saenko, K.: Modeling Relationships in Referential Expressions With Compositional Modular Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1115– 1124 (2017)

    2017

  46. [47]

    In: European Conference on Computer Vision

    Huang, Z., Satoh, S.: LoA-Trans: Enhancing Visual Grounding by Location-Aware Transformers. In: European Conference on Computer Vision. pp. 405–421. Springer (2024)

    2024

  47. [48]

    In: European Conference on Computer Vision

    Hummel, T., Karthik, S., Georgescu, M.I., Akata, Z.: EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrieval. In: European Conference on Computer Vision. pp. 1–17. Springer (2024)

    2024

  48. [49]

    Machines 11, 677 (2023)

    Hussain, M.: YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection. Machines 11, 677 (2023)

    2023

  49. [50]

    Cities152, 1–94 (2024) Abbreviated paper title 19

    Ito, K., Kang, Y., Zhang, Y., Zhang, F., Biljecki, F.: Understanding Urban Perception with Visual Data: A Systematic Review. Cities152, 1–94 (2024) Abbreviated paper title 19

    2024

  50. [51]

    In: European Conference on Computer Vision

    Jiang, H., Lu, Z.: Visual Grounding for Object-Level Generalization in Reinforce- ment Learning. In: European Conference on Computer Vision. pp. 55–72. Springer (2024)

    2024

  51. [52]

    Neural Computing and Applications33(7), 2297–2319 (2021)

    Jirak, D., Biertimpel, D., Kerzel, M., Wermter, S.: Solving Visual Object Ambigu- ities when Pointing: An Unsupervised Learning Approach. Neural Computing and Applications33(7), 2297–2319 (2021)

    2021

  52. [53]

    In: European Conference on Computer Vision

    Ju, Y., Hu, K., Zhang, G., Zhang, G., Jiang, M., Xu, H.: Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Ma- nipulation. In: European Conference on Computer Vision. pp. 222–239. Springer (2024)

    2024

  53. [54]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Kang, S., Kim, J., Kim, J., Hwang, S.J.: Your Large Vision-Language Model Only Needs A Few Attention Heads For Visual Grounding. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 9339–9350 (2025)

    2025

  54. [55]

    In: European Conference on Computer Vision

    Kang, W., Liu, G., Shah, M., Yan, Y.: SegVG: Transferring Object Bounding Box to Segmentation for Visual Grounding. In: European Conference on Computer Vision. pp. 57–75. Springer (2024)

    2024

  55. [56]

    In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing

    Kazemzadeh, S., Ordonez, V., Matten, M., Berg, T.: ReferItGame: Referring to Objects in Photographs of Natural Scenes. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. pp. 787–798 (2014)

    2014

  56. [57]

    In: 2024 International Conference on Information Technology Research and Innovation (ICITRI)

    Kusnanti, E.A., Sierra, E., Putra, G.G.S., Cahyadi, E.S., Haq, A., Purwitasari, D.: Indonesian Lexical Ambiguity in Machine Translation: A Literature Review. In: 2024 International Conference on Information Technology Research and Innovation (ICITRI). pp. 59–64. IEEE (2024)

    2024

  57. [58]

    In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

    Lee, J., Wang, J., Brown, E., Chu, L., Rodriguez, S.S., Froehlich, J.E.: Gaze- PointAR: A context-aware multimodal voice assistant for pronoun disambiguation in wearable augmented reality. In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. pp. 1–20 (2024)

    2024

  58. [59]

    In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics

    Li, Z., Xu, Q., Zhang, D., Song, H., Cai, Y., Qi, Q., Zhou, R., Pan, J., Li, Z., Tu, V., et al.: GroundingGPT: Language Enhanced Multi-modal Grounding Model. In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics. pp. 6657–6678 (2024)

    2024

  59. [60]

    In: Proceedings of the European Conference on Computer Vision

    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: Proceedings of the European Conference on Computer Vision. pp. 740–755 (2014)

    2014

  60. [61]

    ArXiv Preprint ArXiv:1712.09005 (2017)

    Linderman, G.C., Rachh, M., Hoskins, J.G., Steinerberger, S., Kluger, Y.: Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding. ArXiv Preprint ArXiv:1712.09005 (2017)

    Pith/arXiv arXiv 2017

  61. [62]

    ArXiv Preprint ArXiv:2412.19437 (2024)

    Liu, A., Feng, B., Xue, B., Wang, B., Wu, B., Lu, C., Zhao, C., Deng, C., Zhang, C., Ruan, C., et al.: DeepSeek-V3 Technical Report. ArXiv Preprint ArXiv:2412.19437 (2024)

    Pith/arXiv arXiv 2024

  62. [63]

    ArXiv Preprint ArXiv:2405.14295 (2024)

    Liu, C., Wei, H., Chen, J., Kong, L., Ge, Z., Zhu, Z., Zhao, L., Sun, J., Han, C., Zhang, X.: Focus Anywhere for Fine-grained Multi-page Document Understanding. ArXiv Preprint ArXiv:2405.14295 (2024)

    Pith/arXiv arXiv 2024

  63. [64]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Liu, H., Li, C., Li, Y., Lee, Y.J.: Improved baselines with visual instruction tuning. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 26296–26306 (2024)

    2024

  64. [65]

    ArXiv Preprint ArXiv:2405.07801 (2024) 20 L

    Liu, J., Sun, W., Yang, H., Zeng, Z., Liu, C., Zheng, J., Liu, X., Rahmani, H., Sebe, N., Mian, A.: Deep Learning-Based Object Pose Estimation: A Comprehensive Survey. ArXiv Preprint ArXiv:2405.07801 (2024) 20 L. Li et al

    arXiv 2024

  65. [66]

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

    Liu, J., Chen, Q., Wang, Z., Tang, Y., Zhang, Y., Yan, C., Wang, D., Li, X., Zhao, B.: AerialVG: A Challenging Benchmark for Aerial Visual Grounding by Exploring Positional Relations. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5177–5187 (2025)

    2025

  66. [67]

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

    Liu, R., Liu, C., Bai, Y., Yuille, A.L.: CLEVR-Ref+: Diagnosing Visual Reasoning With Referring Expressions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4185–4194 (2019)

    2019

  67. [68]

    In: Proceedings of the European Conference on Computer Vision

    Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Jiang, Q., Li, C., Yang, J., Su, H., et al.: Grounding DINO: Marrying DINO with Grounded Pre-training for Open-Set Object Detection. In: Proceedings of the European Conference on Computer Vision. pp. 38–55 (2024)

    2024

  68. [69]

    IEEE/ASME Transactions on Mechatronics pp

    Liu,Y.,Chen,W.,Bai,Y.,Liang,X.,Li,G.,Gao,W.,Lin,L.:AligningCyberSpace With Physical World: A Comprehensive Survey on Embodied AI. IEEE/ASME Transactions on Mechatronics pp. 1–22 (2025)

    2025

  69. [70]

    International Journal of Robotics & Control Systems2(4) (2022)

    Ma’arif, A., Rahmaniar, W., Fathurrahman, H.I.K., Frisky, A.Z.K., et al.: Un- derstanding of Convolutional Neural Network (CNN): A Review. International Journal of Robotics & Control Systems2(4) (2022)

    2022

  70. [71]

    In: European Conference on Computer Vision

    Magay, A., Tripathi, D., Hao, Y., Fang, Y.: A Light and Smart Wearable Platform with Multimodal Foundation Model for Enhanced Spatial Reasoning in People with Blindness and Low Vision. In: European Conference on Computer Vision. pp. 323–339. Springer (2024)

    2024

  71. [72]

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

    Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A.L., Murphy, K.: Generation and Comprehension of Unambiguous Object Descriptions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 11–20 (2016)

    2016

  72. [73]

    In: European Conference on Computer Vision

    Memmi, M.Y., Slama, R., Berretti, S.: Hand Gesture Recognition Using Dual Graph Hierarchical Edges Representation and Graph Transformer Network. In: European Conference on Computer Vision. pp. 53–68. Springer (2024)

    2024

  73. [74]

    Mo, M., Wang, J., Wang, L., Chen, H., Gu, C., Leng, J., Gao, X.: NexusAD: Exploring the Nexus for Multimodal Perception and Comprehension of Corner CasesinAutonomousDriving.In:ECCV2024WorkshoponMultimodalPerception and Comprehension of Corner Cases in Autonomous Driving (2024)

    2024

  74. [75]

    In: Proceedings of the European Conference on Computer Vision

    Nagaraja, V.K., Morariu, V.I., Davis, L.S.: Modeling Context between Objects for Referring Expression Understanding. In: Proceedings of the European Conference on Computer Vision. pp. 792–807 (2016)

    2016

  75. [76]

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

    Narasimhaswamy, S., Bhattacharya, U., Chen, X., Dasgupta, I., Mitra, S., Hoai, M.: HanDiffuser: Text-to-Image Generation With Realistic Hand Appearances. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2468–2479 (2024)

    2024

  76. [77]

    ArXiv Preprint ArXiv:2304.07193 (2023)

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al.: DINOv2: Learning Robust Visual Features without Supervision. ArXiv Preprint ArXiv:2304.07193 (2023)

    Pith/arXiv arXiv 2023

  77. [78]

    ArXiv Preprint ArXiv:2306.14824 (2023)

    Peng, Z., Wang, W., Dong, L., Hao, Y., Huang, S., Ma, S., Wei, F.: Kosmos-2: Grounding Multimodal Large Language Models to the World. ArXiv Preprint ArXiv:2306.14824 (2023)

    Pith/arXiv arXiv 2023

  78. [79]

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

    Pepikj, B., Stark, M., Gehler, P., Schiele, B.: Occlusion Patterns for Object Class Detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3286–3293 (2013)

    2013

  79. [80]

    Lecture Notes in Computer Science pp

    Pfeifer, R., Iida, F.: Embodied Artificial Intelligence: Trends and Challenges. Lecture Notes in Computer Science pp. 1–26 (2004) Abbreviated paper title 21

    2004

  80. [81]

    In: Proceedings of the IEEE International Conference on Computer Vision

    Plummer, B.A., Wang, L., Cervantes, C.M., Caicedo, J.C., Hockenmaier, J., Lazebnik, S.: Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2641–2649 (2015)

    2015

Showing first 80 references.