{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T16:24:50Z","timestamp":1751473490627,"version":"3.40.5"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031801358"},{"type":"electronic","value":"9783031801365"}],"license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-80136-5_15","type":"book-chapter","created":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T09:38:26Z","timestamp":1733045906000},"page":"211-226","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MRCI: Multi-range Context Interaction for\u00a0Boundary Refinement in\u00a0Image Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8830-8250","authenticated-orcid":false,"given":"Yaqiang","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8949-3101","authenticated-orcid":false,"given":"Wanjun","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianchen","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2016-0901","authenticated-orcid":false,"given":"Jin","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5456-0957","authenticated-orcid":false,"given":"Lianwen","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,1]]},"reference":[{"key":"15_CR1","unstructured":"SpaceNet on amazon web services (AWS). Datasets. https:\/\/spacenet.ai\/datasets\/ (Last modified October 1st, 2018)"},{"key":"15_CR2","doi-asserted-by":"crossref","unstructured":"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\u20138581 (2020)","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: CVPR, pp. 1290\u20131299 (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"15_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1007\/978-3-030-58568-6_39","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Cheng","year":"2020","unstructured":"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\u2013676. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58568-6_39"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213\u20133223 (2016)","DOI":"10.1109\/CVPR.2016.350"},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"He, J., Li, P., Geng, Y., Xie, X.: FastInst: a simple query-based model for real-time instance segmentation. In: CVPR, pp. 23663\u201323672 (2023)","DOI":"10.1109\/CVPR52729.2023.02266"},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.: Mask R-CNN. In: ICCV, pp. 2961\u20132969 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"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\u20132998 (2023)","DOI":"10.1109\/CVPR52729.2023.00292"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"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\u20134421 (2022)","DOI":"10.1109\/CVPR52688.2022.00437"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. In: ICCV, pp. 4015\u20134026 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"15_CR11","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., Girshick, R.: PointRend: image segmentation as rendering. In: CVPR, pp. 9799\u20139808 (2020)","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Li, F., et al.: Mask DINO: towards a unified transformer-based framework for object detection and segmentation. In: CVPR, pp. 3041\u20133050 (2023)","DOI":"10.1109\/CVPR52729.2023.00297"},{"key":"15_CR13","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: CVPR, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Neubeck, A., Van\u00a0Gool, L.: Efficient non-maximum suppression. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol.\u00a03, pp. 850\u2013855 (2006). https:\/\/doi.org\/10.1109\/ICPR.2006.479","DOI":"10.1109\/ICPR.2006.479"},{"key":"15_CR16","unstructured":"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)"},{"key":"15_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"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\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR, pp. 5693\u20135703 (2019)","DOI":"10.1109\/CVPR.2019.00584"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"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\u201313935 (2021)","DOI":"10.1109\/CVPR46437.2021.01371"},{"key":"15_CR20","unstructured":"Vaswani, A., et al.: Attention is all you need. In: NIPS, vol. 30 (2017)"},{"key":"15_CR21","unstructured":"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)"},{"issue":"10","key":"15_CR22","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE TPAMI 43(10), 3349\u20133364 (2020)","journal-title":"IEEE TPAMI"},{"key":"15_CR23","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: RDLNet: a novel and accurate real-world document localization method. In: ACM Multimedia (2024)","DOI":"10.1145\/3664647.3681655"},{"key":"15_CR24","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"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\u201312090 (2021)","journal-title":"NIPS"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Xiong, Y., et al.: UPSNet: a unified panoptic segmentation network. In: CVPR, pp. 8818\u20138826 (2019)","DOI":"10.1109\/CVPR.2019.00902"},{"key":"15_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/978-3-030-58610-2_29","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Y Yuan","year":"2020","unstructured":"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\u2013506. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58610-2_29"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, G., et al.: RefineMask: towards high-quality instance segmentation with fine-grained features. In: CVPR, pp. 6861\u20136869 (2021)","DOI":"10.1109\/CVPR46437.2021.00679"},{"key":"15_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, W., Hu, R.: BAProto: boundary-aware prototype for high-quality instance segmentation. In: ICME, pp. 2333\u20132338. IEEE (2023)","DOI":"10.1109\/ICME55011.2023.00398"},{"key":"15_CR29","doi-asserted-by":"crossref","unstructured":"Zheng, S., et\u00a0al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: CVPR, pp. 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"15_CR30","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., Torralba, A.: Scene parsing through ade20k dataset. In: CVPR, pp. 633\u2013641 (2017)","DOI":"10.1109\/CVPR.2017.544"},{"key":"15_CR31","doi-asserted-by":"crossref","unstructured":"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\u20134401 (2022)","DOI":"10.1109\/CVPR52688.2022.00435"},{"key":"15_CR32","unstructured":"Zou, X., et al.: Segment everything everywhere all at once. In: NIPS, vol. 36 (2024)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-80136-5_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T10:03:18Z","timestamp":1733047398000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-80136-5_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,1]]},"ISBN":["9783031801358","9783031801365"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-80136-5_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,1]]},"assertion":[{"value":"1 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}