[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images

  • Conference paper
  • First Online:
Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15082))

Included in the following conference series:

  • 69 Accesses

Abstract

Hierarchical segmentation entails creating segmentations at varying levels of granularity. We introduce the first hierarchical semantic segmentation dataset with subpart annotations for natural images, which we call SPIN (SubPartImageNet). We also introduce two novel evaluation metrics to evaluate how well algorithms capture spatial and semantic relationships across hierarchical levels. We benchmark modern models across three different tasks and analyze their strengths and weaknesses across objects, parts, and subparts. To facilitate community-wide progress, we publicly release our dataset at https://joshmyersdean.github.io/spin/index.html.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 54.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 69.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    No code is publicly available for VDT, and ViRReq does not offer complete code. At the time of writing, Semantic-SAM has not released their semantic prediction code.

  2. 2.

    We prompted GPT-4 with “Please list the canonical subparts of a <object>-<part>. Only include subparts that are clearly visible and recognizable to a layperson.”.

  3. 3.

    For a quadruped, for instance, pairs such as \(\{\)(eyes, head), (chest, torso), (torso, quadruped)\(\}\) could be present in \(\mathcal {R}\).

  4. 4.

    For subparts, we refer to the parent object rather than the parent part, as broader context aids in processing finer details [47].

  5. 5.

    Results are shown in the supplementary materials for two prompts asking “Is the category not present” and “Is the [different category] present”.

References

  1. Explore images. https://support.apple.com/guide/iphone/use-voiceover-for-images-and-videos-iph37e6b3844/ios

  2. Achiam, J., et al.: GPT-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

  3. Berglund, L., et al.: The reversal curse: LLMs trained on “a is b” fail to learn “b is a”. arXiv preprint arXiv:2309.12288 (2023)

  4. Cai, M., et al.: Making large multimodal models understand arbitrary visual prompts. In: IEEE Conference on Computer Vision and Pattern Recognition (2024)

    Google Scholar 

  5. Chang, A.X., et al.: ShapeNet: an information-rich 3D moel repository. arXiv preprint arXiv:1512.03012 (2015)

  6. 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)

  7. Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.: Detect what you can: detecting and representing objects using holistic models and body parts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1971–1978 (2014)

    Google Scholar 

  8. Deitke, M., et al.: RoboTHOR: an open simulation-to-real embodied AI platform. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  9. Deng, B., Genova, K., Yazdani, S., Bouaziz, S., Hinton, G., Tagliasacchi, A.: CVXNet: learnable convex decomposition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 31–44 (2020)

    Google Scholar 

  10. Desai, K., Nickel, M., Rajpurohit, T., Johnson, J., Vedantam, S.R.: Hyperbolic image-text representations. In: International Conference on Machine Learning, pp. 7694–7731. PMLR (2023)

    Google Scholar 

  11. Ding, M., et al.: Visual dependency transformers: Dependency tree emerges from reversed attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14528–14539 (2023)

    Google Scholar 

  12. Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  13. Geng, H., et al.: GAPartNet: cross-category domain-generalizable object perception and manipulation via generalizable and actionable parts. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7081–7091 (2023)

    Google Scholar 

  14. de Geus, D., Meletis, P., Lu, C., Wen, X., Dubbelman, G.: Part-aware panoptic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5485–5494 (2021)

    Google Scholar 

  15. Gong, K., Liang, X., Li, Y., Chen, Y., Yang, M., Lin, L.: Instance-level human parsing via part grouping network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 770–785 (2018)

    Google Scholar 

  16. He, J., Chen, J., Lin, M.X., Yu, Q., Yuille, A.L.: Compositor: bottom-up clustering and compositing for robust part and object segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11259–11268 (2023)

    Google Scholar 

  17. He, J., et al.: PartImageNet: a large, high-quality dataset of parts. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13668, pp. 128–145. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-20074-8_8

    Chapter  Google Scholar 

  18. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  19. Hong, Y., Li, Q., Zhu, S.C., Huang, S.: VLGrammar: grounded grammar induction of vision and language. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1665–1674 (2021)

    Google Scholar 

  20. Hong, Y., Yi, L., Tenenbaum, J., Torralba, A., Gan, C.: PTR: a benchmark for part-based conceptual, relational, and physical reasoning. Adv. Neural. Inf. Process. Syst. 34, 17427–17440 (2021)

    Google Scholar 

  21. Jia, M., et al.: Fashionpedia: ontology, segmentation, and an attribute localization dataset. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part I. LNCS, vol. 12346, pp. 316–332. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_19

    Chapter  Google Scholar 

  22. Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4015–4026 (2023)

    Google Scholar 

  23. Koo, J., Huang, I., Achlioptas, P., Guibas, L.J., Sung, M.: PartGlot: learning shape part segmentation from language reference games. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16505–16514 (2022)

    Google Scholar 

  24. Lai, X., et al.: LISA: reasoning segmentation via large language model. arXiv preprint arXiv:2308.00692 (2023)

  25. Lee, J., Peng, Y.H., Herskovitz, J., Guo, A.: Image explorer: multi-layered touch exploration to make images accessible. In: Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2021. Association for Computing Machinery, New York (2021). https://doi.org/10.1145/3441852.3476548

  26. Li, F., et al.: Semantic-SAM: segment and recognize anything at any granularity. arXiv preprint arXiv:2307.04767 (2023)

  27. Li, L., Zhou, T., Wang, W., Li, J., Yang, Y.: Deep hierarchical semantic segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1246–1257 (2022)

    Google Scholar 

  28. Li, T., Gupta, V., Mehta, M., Srikumar, V.: A logic-driven framework for consistency of neural models. arXiv preprint arXiv:1909.00126 (2019)

  29. Li, X., Xu, S., Yang, Y., Cheng, G., Tong, Y., Tao, D.: Panoptic-partformer: learning a unified model for panoptic part segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13687, pp. 729–747. Springer, Cham (2022)

    Chapter  Google Scholar 

  30. Li, X., et al.: Panoptic-PartFormer++: a unified and decoupled view for panoptic part segmentation. arXiv preprint arXiv:2301.00954 (2023)

  31. Liang, X., Gong, K., Shen, X., Lin, L.: Look into person: joint body parsing & pose estimation network and a new benchmark. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 871–885 (2018)

    Article  Google Scholar 

  32. Liang, X., Shen, X., Feng, J., Lin, L., Yan, S.: Semantic object parsing with graph LSTM. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part I. LNCS, vol. 9905, pp. 125–143. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_8

    Chapter  Google Scholar 

  33. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

    Chapter  Google Scholar 

  34. Liu, J., Min, S., Zettlemoyer, L., Choi, Y., Hajishirzi, H.: Infini-gram: scaling unbounded n-gram language models to a trillion tokens. arXiv preprint arXiv:2401.17377 (2024)

  35. Liu, Q., et al.: CGPart: a part segmentation dataset based on 3D computer graphics models. arXiv preprint arXiv:2103.14098 (2021)

  36. Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, vol. 2, pp. 416–423. IEEE (2001)

    Google Scholar 

  37. Michieli, U., Zanuttigh, P.: Edge-aware graph matching network for part-based semantic segmentation. Int. J. Comput. Vision 130(11), 2797–2821 (2022)

    Article  Google Scholar 

  38. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). https://doi.org/10.1145/219717.219748

    Article  Google Scholar 

  39. Mo, K., et al.: PartNet: a large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 909–918 (2019)

    Google Scholar 

  40. Mo, K., et al.: PartNet: a large-scale benchmark for fine-grained and hierarchical part-level 3D object understanding. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  41. Myers-Dean, J., Fan, Y., Price, B., Chan, W., Gurari, D.: Interactive segmentation for diverse gesture types without context. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 7198–7208 (2024)

    Google Scholar 

  42. Nair, V., Zhu, H.H., Smith, B.A.: ImageAssist: tools for enhancing touchscreen-based image exploration systems for blind and low vision users. In: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023. Association for Computing Machinery, New York (2023). https://doi.org/10.1145/3544548.3581302

  43. Peng, Z., et al.: Kosmos-2: grounding multimodal large language models to the world. arXiv preprint arXiv:2306.14824 (2023)

  44. Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748–8763. PMLR (2021)

    Google Scholar 

  45. Ramanathan, V., et al.: PACO: parts and attributes of common objects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7141–7151 (2023)

    Google Scholar 

  46. Rasheed, H., et al.: GLaMM: pixel grounding large multimodal model. arXiv preprint arXiv:2311.03356 (2023)

  47. Raymond, W., Gibbs, J., Matlock, T.: Psycholinguistics and mental representations (2000)

    Google Scholar 

  48. Ren, Z., et al.: PixeLLM: pixel reasoning with large multimodal model (2023)

    Google Scholar 

  49. Sennrich, R., Haddow, B., Birch, A.: Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909 (2015)

  50. Song, X., et al.: ApolloCar3D: a large 3D car instance understanding benchmark for autonomous driving. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5452–5462 (2019)

    Google Scholar 

  51. Sun, P., et al.: Going denser with open-vocabulary part segmentation. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 15407–15419 (2023). https://api.semanticscholar.org/CorpusID:258762519

  52. Tang, C., Xie, L., Zhang, X., Hu, X., Tian, Q.: Visual recognition by request. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15265–15274 (2023)

    Google Scholar 

  53. Tong, K., Wu, Y.: Deep learning-based detection from the perspective of small or tiny objects: a survey. Image Vis. Comput. 123, 104471 (2022)

    Article  Google Scholar 

  54. Touvron, H., et al.: LLaMA: open and efficient foundation language models (2023)

    Google Scholar 

  55. Tsogkas, S., Kokkinos, I., Papandreou, G., Vedaldi, A.: Deep learning for semantic part segmentation with high-level guidance. arXiv preprint arXiv:1505.02438 (2015)

  56. Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-UCSD birds-200-2011 dataset (2011)

    Google Scholar 

  57. Wang, J., Yuille, A.L.: Semantic part segmentation using compositional model combining shape and appearance. In: Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 1788–1797 (2015)

    Google Scholar 

  58. Wang, P., Shen, X., Lin, Z., Cohen, S., Price, B., Yuille, A.L.: Joint object and part segmentation using deep learned potentials. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1573–1581 (2015)

    Google Scholar 

  59. Wang, W., et al.: CogVLM: visual expert for pretrained language models. arXiv preprint arXiv:2311.03079 (2023)

  60. Wang, X., Li, S., Kallidromitis, K., Kato, Y., Kozuka, K., Darrell, T.: Hierarchical open-vocabulary universal image segmentation. Adv. Neural Inf. Process. Syst. 36 (2024)

    Google Scholar 

  61. Wei, M., Yue, X., Zhang, W., Kong, S., Liu, X., Pang, J.: OV-PARTS: towards open-vocabulary part segmentation. Adv. Neural Inf. Process. Syst 36 (2024)

    Google Scholar 

  62. Wu, T.H., et al.: See, say, and segment: teaching LMMs to overcome false premises. arXiv preprint arXiv:2312.08366 (2023)

  63. Xiang, F., et al.: SAPIEN: a simulated part-based interactive environment. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  64. You, H., et al.: FERRET: refer and ground anything anywhere at any granularity. arXiv preprint arXiv:2310.07704 (2023)

  65. Yu, F., Liu, K., Zhang, Y., Zhu, C., Xu, K.: PartNet: a recursive part decomposition network for fine-grained and hierarchical shape segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9491–9500 (2019)

    Google Scholar 

  66. Yuan, Y., et al.: Osprey: pixel understanding with visual instruction tuning (2023)

    Google Scholar 

  67. Zhao, J., Li, J., Cheng, Y., Sim, T., Yan, S., Feng, J.: Understanding humans in crowded scenes: deep nested adversarial learning and a new benchmark for multi-human parsing. In: Proceedings of the 26th ACM international conference on Multimedia, pp. 792–800 (2018)

    Google Scholar 

  68. Zheng, S., Yang, F., Kiapour, M.H., Piramuthu, R.: ModaNet: a large-scale street fashion dataset with polygon annotations. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 1670–1678 (2018)

    Google Scholar 

  69. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 633–641 (2017)

    Google Scholar 

Download references

Acknowledgments

This work was supported by Adobe Research Gift Funds and utilized the Blanca condo computing resource at the University of Colorado Boulder. Josh Myers-Dean is supported by a NSF GRFP fellowship (#1917573). We thank the crowdworkers for contributing their time for the construction of SPIN and the authors of our benchmarked models for open-sourcing their work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Josh Myers-Dean .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 47296 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Myers-Dean, J., Reynolds, J., Price, B., Fan, Y., Gurari, D. (2025). SPIN: Hierarchical Segmentation with Subpart Granularity in Natural Images. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15082. Springer, Cham. https://doi.org/10.1007/978-3-031-72691-0_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-72691-0_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72690-3

  • Online ISBN: 978-3-031-72691-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics