Skip to main content

MRCI: Multi-range Context Interaction for Boundary Refinement in Image Segmentation

  • Conference paper
  • First Online:
Pattern Recognition (ICPR 2024)

Abstract

In the era of foundational image segmentation models, there is a pressing need to leverage the outputs of these models and enhance the boundary accuracy of domain-specific segmentation results using lightweight post-processing techniques. Numerous existing boundary refinement approaches neglect the significance of incorporating diverse contextual scopes and global knowledge, resulting in restricted adaptability to different coarse segmentation errors. Moreover, the prevailing models are often lacking in lightweight design. To address these challenges, we propose a novel framework called Multi-Range Context Interaction (MRCI) that aims to refine the boundaries of predicted masks by incorporating comprehensive context knowledge while maintaining computational efficiency. Our approach utilizes a multi-range context-aware strategy to extract more informative local features and incorporates global knowledge prompts to guide the boundary refinement process. Experimental results on the widely used Cityscapes, ADE20K and satellite remote sensing dataset SpaceNet demonstrate the effectiveness of our approach, achieving top-tier Average Precision (AP) and mean IoU among the current state-of-the-art boundary refinement models while utilizing only 4M parameters. The source code will be available.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /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
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. SpaceNet on amazon web services (AWS). Datasets. https://spacenet.ai/datasets/ (Last modified October 1st, 2018)

  2. Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., Yan, Y.: BlendMask: top-down meets bottom-up for instance segmentation. In: CVPR, pp. 8573–8581 (2020)

    Google Scholar 

  3. Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR, pp. 1290–1299 (2022)

    Google Scholar 

  4. Cheng, T., Wang, X., Huang, L., Liu, W.: Boundary-preserving mask R-CNN. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 660–676. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_39

    Chapter  Google Scholar 

  5. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213–3223 (2016)

    Google Scholar 

  6. He, J., Li, P., Geng, Y., Xie, X.: FastInst: a simple query-based model for real-time instance segmentation. In: CVPR, pp. 23663–23672 (2023)

    Google Scholar 

  7. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV, pp. 2961–2969 (2017)

    Google Scholar 

  8. Jain, J., Li, J., Chiu, M.T., Hassani, A., Orlov, N., Shi, H.: OneFormer: one transformer to rule universal image segmentation. In: CVPR, pp. 2989–2998 (2023)

    Google Scholar 

  9. Ke, L., Danelljan, M., Li, X., Tai, Y.W., Tang, C.K., Yu, F.: Mask transfiner for high-quality instance segmentation. In: CVPR, pp. 4412–4421 (2022)

    Google Scholar 

  10. Kirillov, A., et al.: Segment anything. In: ICCV, pp. 4015–4026 (2023)

    Google Scholar 

  11. Kirillov, A., Wu, Y., He, K., Girshick, R.: PointRend: image segmentation as rendering. In: CVPR, pp. 9799–9808 (2020)

    Google Scholar 

  12. Li, F., et al.: Mask DINO: towards a unified transformer-based framework for object detection and segmentation. In: CVPR, pp. 3041–3050 (2023)

    Google Scholar 

  13. Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: CVPR, pp. 8759–8768 (2018)

    Google Scholar 

  14. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431–3440 (2015)

    Google Scholar 

  15. Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 3, pp. 850–855 (2006). https://doi.org/10.1109/ICPR.2006.479

  16. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, vo. 28 (2015)

    Google Scholar 

  17. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  18. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR, pp. 5693–5703 (2019)

    Google Scholar 

  19. Tang, C., Chen, H., Li, X., Li, J., Zhang, Z., Hu, X.: Look closer to segment better: Boundary patch refinement for instance segmentation. In: CVPR, pp. 13926–13935 (2021)

    Google Scholar 

  20. Vaswani, A., et al.: Attention is all you need. In: NIPS, vol. 30 (2017)

    Google Scholar 

  21. Wan, Q., Huang, Z., Kang, B., Feng, J., Zhang, L.: Harnessing diffusion models for visual perception with meta prompts. arXiv preprint arXiv:2312.14733 (2023)

  22. Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE TPAMI 43(10), 3349–3364 (2020)

    Article  Google Scholar 

  23. Wu, Y., et al.: RDLNet: a novel and accurate real-world document localization method. In: ACM Multimedia (2024)

    Google Scholar 

  24. Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: SegFormer: simple and efficient design for semantic segmentation with transformers. NIPS 34, 12077–12090 (2021)

    Google Scholar 

  25. Xiong, Y., et al.: UPSNet: a unified panoptic segmentation network. In: CVPR, pp. 8818–8826 (2019)

    Google Scholar 

  26. Yuan, Y., Xie, J., Chen, X., Wang, J.: SegFix: model-agnostic boundary refinement for segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12357, pp. 489–506. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58610-2_29

    Chapter  Google Scholar 

  27. Zhang, G., et al.: RefineMask: towards high-quality instance segmentation with fine-grained features. In: CVPR, pp. 6861–6869 (2021)

    Google Scholar 

  28. Zhang, Y., Yang, W., Hu, R.: BAProto: boundary-aware prototype for high-quality instance segmentation. In: ICME, pp. 2333–2338. IEEE (2023)

    Google Scholar 

  29. Zheng, S., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR, pp. 6881–6890 (2021)

    Google Scholar 

  30. Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: CVPR, pp. 633–641 (2017)

    Google Scholar 

  31. Zhu, C., Zhang, X., Li, Y., Qiu, L., Han, K., Han, X.: SharpContour: a contour-based boundary refinement approach for efficient and accurate instance segmentation. In: CVPR, pp. 4392–4401 (2022)

    Google Scholar 

  32. Zou, X., et al.: Segment everything everywhere all at once. In: NIPS, vol. 36 (2024)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qinghua Zheng .

Editor information

Editors and Affiliations

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

Wu, Y., Lyu, W., Liang, X., Zheng, Q., Wei, J., Jin, L. (2025). MRCI: Multi-range Context Interaction for Boundary Refinement in Image Segmentation. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15333. Springer, Cham. https://doi.org/10.1007/978-3-031-80136-5_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-80136-5_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-80135-8

  • Online ISBN: 978-3-031-80136-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics