{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:46:22Z","timestamp":1782949582320,"version":"3.54.5"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698554","type":"print"},{"value":"9789819698561","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-9856-1_26","type":"book-chapter","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T12:38:42Z","timestamp":1753187922000},"page":"305-317","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Zero-Shot Scene Graph Generation with Bias Correction and Unseen Space Optimization"],"prefix":"10.1007","author":[{"given":"Xinyue","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinsai","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaorong","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Xu, D., Zhu, Y., Choy, C.B., Fei-Fei, L.: Scene graph generation by iterative message passing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5410\u20135419. IEEE, Honolulu, HI (2017)","DOI":"10.1109\/CVPR.2017.330"},{"key":"26_CR2","doi-asserted-by":"crossref","unstructured":"Knyazev, B., De Vries, H., Cangea, C., Taylor, G.W., Courville, A., Belilovsky, E.: Graph density-aware losses for novel compositions in scene graph generation. arXiv preprint arXiv:2005.08230 (2020)","DOI":"10.5244\/C.34.99"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Zellers, R., Yatskar, M., Thomson, S., Choi, Y.: Neural motifs: scene graph parsing with global context. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5831\u20135840. IEEE, Salt Lake City, UT (2018)","DOI":"10.1109\/CVPR.2018.00611"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Knyazev, B., de Vries, H., Cangea, C., Taylor, G.W., Courville, A., Belilovsky, E.: Generative compositional augmentations for scene graph prediction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15827\u201315837. IEEE, Montreal, Canada (2021)","DOI":"10.1109\/ICCV48922.2021.01553"},{"issue":"1","key":"26_CR5","first-page":"1","volume":"20","author":"J Li","year":"2023","unstructured":"Li, J., Wang, Y., Li, W.: Zero-shot scene graph generation via triplet calibration and reduction. ACM Trans. Multimed. Comput. Commun. Appl. 20(1), 1\u201321 (2023)","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Zhang, H., Kyaw, Z., Chang, S.F., Chua, T.S.: Visual translation embedding network for visual relation detection. In: Proceedings of the CVPR, pp. 5532\u20135540. IEEE, Honolulu, HI (2017)","DOI":"10.1109\/CVPR.2017.331"},{"issue":"11","key":"26_CR7","doi-asserted-by":"publisher","first-page":"3820","DOI":"10.1109\/TPAMI.2020.2992222","volume":"43","author":"ZS Hung","year":"2021","unstructured":"Hung, Z.S., Mallya, A., Lazebnik, S.: Contextual translation embedding for visual relationship detection and scene graph generation. IEEE Trans. Pattern Anal. Mach. Intell. 43(11), 3820\u20133832 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Tang, K., Niu, Y., Huang, J., Shi, J., Zhang, H.: Unbiased scene graph generation from biased training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3716\u20133725. IEEE, Seattle, WA (2020)","DOI":"10.1109\/CVPR42600.2020.00377"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Suhail, M., et al.: Energy-based learning for scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13936\u201313945. IEEE, Nashville, TN (2021)","DOI":"10.1109\/CVPR46437.2021.01372"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Li, R., Zhang, S., Wan, B., He, X.: Bipartite graph network with adaptive message passing for unbiased scene graph generation. In: Proceedings of the CVPR, pp. 11109\u201311119. IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.01096"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Dong, X., Gan, T., Song, X., Wu, J., Cheng, Y., Nie, L.: Stacked hybrid-attention and group collaborative learning for unbiased scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. (2022)","DOI":"10.1109\/CVPR52688.2022.01882"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Teng, Y., Wang, L.: Structured sparse R-CNN for direct scene graph generation. In: Proceedings CVPR, pp. 19437\u201319446. IEEE, New Orleans, LA (2022)","DOI":"10.1109\/CVPR52688.2022.01883"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Goel, A., Fernando, B., Keller, F., Bilen, H.: Not all relations are equal: Mining informative labels for scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15596\u201315606. IEEE, New Orleans, LA (2022)","DOI":"10.1109\/CVPR52688.2022.01515"},{"key":"26_CR14","doi-asserted-by":"publisher","first-page":"8646","DOI":"10.1109\/TMM.2023.3239229","volume":"25","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Xu, X., Wang, G., Yang, Y., Shen, H.T.: Quaternion relation embedding for scene graph generation. IEEE Trans. Multimedia 25, 8646\u20138656 (2023)","journal-title":"IEEE Trans. Multimedia"},{"key":"26_CR15","unstructured":"Di, Q., Ma, W., Qi, Z., Hou, T., Shan, Y., Wang, H.: Towards unseen triples: effective text-image-joint learning for scene graph generation. arXiv preprint arXiv:2306.13420 (2023)"},{"key":"26_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.128042","volume":"599","author":"R Peng","year":"2024","unstructured":"Peng, R., et al.: A causality guided loss for imbalanced learning in scene graph generation. Neurocomputing 599, 128042 (2024)","journal-title":"Neurocomputing"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Khan, M.J., Breslin, J.G., Curry, E.: Knowzrel: common sense knowledge-based zero-shot relationship retrieval for generalised scene graph generation. IEEE Trans. Artif. Intell., 1\u201311 (2025)","DOI":"10.1109\/TAI.2025.3544177"},{"key":"26_CR18","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/s11263-016-0981-7","volume":"123","author":"R Krishna","year":"2017","unstructured":"Krishna, R., Zhu, Y., Groth, O., Johnson, J., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. Int. J. Comput. Vision 123, 32\u201373 (2017)","journal-title":"Int. J. Comput. Vision"},{"key":"26_CR19","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28. MIT Press, Montreal, Canada (2015)"},{"key":"26_CR20","unstructured":"Kiryo, R., Niu, G., Du Plessis, M.C., Sugiyama, M.: Positive-unlabeled learning with non-negative risk estimator. In: Advances in Neural Information Processing Systems, vol. 30. MIT Press, Long Beach, CA (2017)"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Hao, S., Han, K., Wong, K.Y.K.: Learning attention as disentangler for composi-tional zero-shot learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15315\u201315324. IEEE, Vancouver, Canada (2023)","DOI":"10.1109\/CVPR52729.2023.01470"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9856-1_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:40:58Z","timestamp":1782949258000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9856-1_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698554","9789819698561"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9856-1_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"23 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}