{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:09:49Z","timestamp":1743059389776,"version":"3.40.3"},"publisher-location":"Cham","reference-count":69,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031263477"},{"type":"electronic","value":"9783031263484"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26348-4_18","type":"book-chapter","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T07:03:47Z","timestamp":1678259027000},"page":"297-315","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Continuous Self-study: Scene Graph Generation with\u00a0Self-knowledge Distillation and\u00a0Spatial Augmentation"],"prefix":"10.1007","author":[{"given":"Yuan","family":"Lv","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yajing","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shusen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yingjian","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dengke","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"key":"18_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 (2017)","DOI":"10.1109\/CVPR.2017.330"},{"key":"18_CR2","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 (2018)","DOI":"10.1109\/CVPR.2018.00611"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Ye, K., Kovashka, A.: Linguistic structures as weak supervision for visual scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8289\u20138299 (2021)","DOI":"10.1109\/CVPR46437.2021.00819"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Wang, Y.S., Liu, C., Zeng, X., Yuille, A.: Scene graph parsing as dependency parsing. arXiv preprint arXiv:1803.09189 (2018)","DOI":"10.18653\/v1\/N18-1037"},{"key":"18_CR5","doi-asserted-by":"crossref","unstructured":"Marino, K., Salakhutdinov, R., Gupta, A.: The more you know: using knowledge graphs for image classification. arXiv preprint arXiv:1612.04844 (2016)","DOI":"10.1109\/CVPR.2017.10"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Wang, W., Wang, R., Shan, S., Chen, X.: Exploring context and visual pattern of relationship for scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8188\u20138197 (2019)","DOI":"10.1109\/CVPR.2019.00838"},{"key":"18_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-030-01246-5_21","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Li","year":"2018","unstructured":"Li, Y., Ouyang, W., Zhou, B., Shi, J., Zhang, C., Wang, X.: Factorizable Net: an efficient subgraph-based framework for scene graph generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 346\u2013363. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_21"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Gu, J., Zhao, H., Lin, Z., Li, S., Cai, J., Ling, M.: Scene graph generation with external knowledge and image reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1969\u20131978 (2019)","DOI":"10.1109\/CVPR.2019.00207"},{"key":"18_CR9","doi-asserted-by":"crossref","unstructured":"Chen, T., Yu, W., Chen, R., Lin, L.: Knowledge-embedded routing network for scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6163\u20136171 (2019)","DOI":"10.1109\/CVPR.2019.00632"},{"key":"18_CR10","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. PP(99), 1 (2020)"},{"key":"18_CR11","doi-asserted-by":"crossref","unstructured":"Fang, Y., Kuan, K., Lin, J., Tan, C., Chandrasekhar, V.: Object detection meets knowledge graphs. In: International Joint Conferences on Artificial Intelligence (2017)","DOI":"10.24963\/ijcai.2017\/230"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Dai, B., Zhang, Y., Lin, D.: Detecting visual relationships with deep relational networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3076\u20133086 (2017)","DOI":"10.1109\/CVPR.2017.352"},{"key":"18_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., Ouyang, W., Zhou, B., Wang, K., Wang, X.: Scene graph generation from objects, phrases and region captions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1261\u20131270 (2017)","DOI":"10.1109\/ICCV.2017.142"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Chiou, M.J., Liu, Z., Yin, Y., Liu, A.A., Zimmermann, R.: Zero-shot multi-view indoor localization via graph location networks. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3431\u20133440 (2020)","DOI":"10.1145\/3394171.3413856"},{"key":"18_CR15","doi-asserted-by":"crossref","unstructured":"Armeni, I., et al.: 3D scene graph: a structure for unified semantics, 3d space, and camera. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5664\u20135673 (2019)","DOI":"10.1109\/ICCV.2019.00576"},{"key":"18_CR16","doi-asserted-by":"crossref","unstructured":"Schuster, S., Krishna, R., Chang, A., Fei-Fei, L., Manning, C.D.: Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In: Proceedings of the Fourth Workshop on Vision and Language, pp. 70\u201380 (2015)","DOI":"10.18653\/v1\/W15-2812"},{"key":"18_CR17","unstructured":"Norcliffe-Brown, W., Vafeias, E., Parisot, S.: Learning conditioned graph structures for interpretable visual question answering. arXiv preprint arXiv:1806.07243 (2018)"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Yu, J., Wang, Y., Sun, Y., Hu, Y., Wu, Q.: Mucko: multi-layer cross-modal knowledge reasoning for fact-based visual question answering. arXiv preprint arXiv:2006.09073 (2020)","DOI":"10.24963\/ijcai.2020\/153"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Yang, X., Tang, K., Zhang, H., Cai, J.: Auto-encoding scene graphs for image captioning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10685\u201310694 (2019)","DOI":"10.1109\/CVPR.2019.01094"},{"key":"18_CR20","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 (2020)","DOI":"10.1109\/CVPR42600.2020.00377"},{"key":"18_CR21","doi-asserted-by":"crossref","unstructured":"Guo, Y., et al.: From general to specific: informative scene graph generation via balance adjustment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16383\u201316392 (2021)","DOI":"10.1109\/ICCV48922.2021.01607"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Yu, J., Chai, Y., Wang, Y., Hu, Y., Wu, Q.: CogTree: cognition tree loss for unbiased scene graph generation. arXiv preprint arXiv:2009.07526 (2020)","DOI":"10.24963\/ijcai.2021\/176"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Tang, K., Zhang, H., Wu, B., Luo, W., Liu, W.: Learning to compose dynamic tree structures for visual contexts. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6619\u20136628 (2019)","DOI":"10.1109\/CVPR.2019.00678"},{"key":"18_CR24","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 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5532\u20135540 (2017)","DOI":"10.1109\/CVPR.2017.331"},{"key":"18_CR25","doi-asserted-by":"crossref","unstructured":"Chiou, M.J., Ding, H., Yan, H., Wang, C., Zimmermann, R., Feng, J.: Recovering the unbiased scene graphs from the biased ones. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 1581\u20131590 (2021)","DOI":"10.1145\/3474085.3475297"},{"key":"18_CR26","unstructured":"Krishna, R., et al.: Visual genome: connecting language and vision using crowdsourced dense image annotations. arXiv preprint arXiv:1602.07332 (2016)"},{"key":"18_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"690","DOI":"10.1007\/978-3-030-01246-5_41","volume-title":"Computer Vision \u2013 ECCV 2018","author":"J Yang","year":"2018","unstructured":"Yang, J., Lu, J., Lee, S., Batra, D., Parikh, D.: Graph R-CNN for scene graph\u00a0generation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11205, pp. 690\u2013706. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01246-5_41"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Ji, M., Shin, S., Hwang, S., Park, G., Moon, I.C.: Refine myself by teaching myself: feature refinement via self-knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10664\u201310673 (2021)","DOI":"10.1109\/CVPR46437.2021.01052"},{"key":"18_CR29","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive representation distillation. arXiv preprint arXiv:1910.10699 (2019)"},{"key":"18_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., Ma, K.: Be your own teacher: improve the performance of convolutional neural networks via self distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3713\u20133722 (2019)","DOI":"10.1109\/ICCV.2019.00381"},{"key":"18_CR31","doi-asserted-by":"crossref","unstructured":"Peng, B., et al.: Correlation congruence for knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5007\u20135016 (2019)","DOI":"10.1109\/ICCV.2019.00511"},{"key":"18_CR32","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"18_CR33","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"18_CR34","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"852","DOI":"10.1007\/978-3-319-46448-0_51","volume-title":"Computer Vision \u2013 ECCV 2016","author":"C Lu","year":"2016","unstructured":"Lu, C., Krishna, R., Bernstein, M., Fei-Fei, L.: Visual relationship detection with language priors. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 852\u2013869. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_51"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Shi, J., Zhang, H., Li, J.: Explainable and explicit visual reasoning over scene graphs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8376\u20138384 (2019)","DOI":"10.1109\/CVPR.2019.00857"},{"key":"18_CR36","doi-asserted-by":"crossref","unstructured":"Krishna, R., Chami, I., Bernstein, M., Fei-Fei, L.: Referring relationships. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6867\u20136876 (2018)","DOI":"10.1109\/CVPR.2018.00718"},{"key":"18_CR37","doi-asserted-by":"crossref","unstructured":"Johnson, J., Gupta, A., Fei-Fei, L.: Image generation from scene graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1219\u20131228 (2018)","DOI":"10.1109\/CVPR.2018.00133"},{"key":"18_CR38","unstructured":"Woo, S., Kim, D., Cho, D., Kweon, I.S.: LinkNet: relational embedding for scene graph. arXiv preprint arXiv:1811.06410 (2018)"},{"key":"18_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3310\/hta19930","volume":"19","author":"G Dunn","year":"2015","unstructured":"Dunn, G., et al.: Evaluation and validation of social and psychological markers in randomised trials of complex interventions in mental health: a methodological research programme. Health Technol. Assess. (Winchester, England) 19, 1\u2013116 (2015)","journal-title":"Health Technol. Assess. (Winchester, England)"},{"key":"18_CR40","doi-asserted-by":"crossref","unstructured":"Qi, M., Li, W., Yang, Z., Wang, Y., Luo, J.: Attentive relational networks for mapping images to scene graphs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3957\u20133966 (2019)","DOI":"10.1109\/CVPR.2019.00408"},{"key":"18_CR41","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"18_CR42","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)"},{"key":"18_CR43","unstructured":"Zagoruyko, S., Komodakis, N.: Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer. arXiv preprint arXiv:1612.03928 (2016)"},{"key":"18_CR44","unstructured":"Kim, J., Park, S., Kwak, N.: Paraphrasing complex network: network compression via factor transfer. arXiv preprint arXiv:1802.04977 (2018)"},{"key":"18_CR45","doi-asserted-by":"crossref","unstructured":"Ahn, S., Hu, S.X., Damianou, A., Lawrence, N.D., Dai, Z.: Variational information distillation for knowledge transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9163\u20139171 (2019)","DOI":"10.1109\/CVPR.2019.00938"},{"key":"18_CR46","doi-asserted-by":"crossref","unstructured":"Yim, J., Joo, D., Bae, J., Kim, J.: A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4133\u20134141 (2017)","DOI":"10.1109\/CVPR.2017.754"},{"key":"18_CR47","unstructured":"Koratana, A., Kang, D., Bailis, P., Zaharia, M.: LIT: learned intermediate representation training for model compression. In: International Conference on Machine Learning, pp. 3509\u20133518. PMLR (2019)"},{"key":"18_CR48","doi-asserted-by":"crossref","unstructured":"Liu, Y., Cao, J., Li, B., Yuan, C., Hu, W., Li, Y., Duan, Y.: Knowledge distillation via instance relationship graph. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7096\u20137104 (2019)","DOI":"10.1109\/CVPR.2019.00726"},{"key":"18_CR49","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3967\u20133976 (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"18_CR50","doi-asserted-by":"crossref","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2020)","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"18_CR51","doi-asserted-by":"crossref","unstructured":"Pham, H., Dai, Z., Xie, Q., Le, Q.V.: Meta pseudo labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11557\u201311568 (2021)","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"18_CR52","first-page":"5565","volume":"33","author":"TB Xu","year":"2019","unstructured":"Xu, T.B., Liu, C.L.: Data-distortion guided self-distillation for deep neural networks. Proceed. AAAI Conf. Artif. Intell. 33, 5565\u20135572 (2019)","journal-title":"Proceed. AAAI Conf. Artif. Intell."},{"key":"18_CR53","doi-asserted-by":"crossref","unstructured":"Yun, S., Park, J., Lee, K., Shin, J.: Regularizing class-wise predictions via self-knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13876\u201313885 (2020)","DOI":"10.1109\/CVPR42600.2020.01389"},{"key":"18_CR54","unstructured":"Lee, H., Hwang, S.J., Shin, J.: Self-supervised label augmentation via input transformations. In: International Conference on Machine Learning, pp. 5714\u20135724. PMLR (2020)"},{"key":"18_CR55","unstructured":"Lan, X., Zhu, X., Gong, S.: Knowledge distillation by on-the-fly native ensemble. arXiv preprint arXiv:1806.04606 (2018)"},{"key":"18_CR56","unstructured":"Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"key":"18_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848\u20136856 (2018)","DOI":"10.1109\/CVPR.2018.00716"},{"key":"18_CR58","first-page":"91","volume":"28","author":"S Ren","year":"2015","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural. Inf. Process. Syst. 28, 91\u201399 (2015)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"18_CR59","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1088\/0143-0807\/30\/5\/014","volume":"30","author":"M Vollmer","year":"2009","unstructured":"Vollmer, M.: Newton\u2019s law of cooling revisited. Eur. J. Phys. 30, 1063 (2009)","journal-title":"Eur. J. Phys."},{"key":"18_CR60","doi-asserted-by":"crossref","unstructured":"Yang, Z., Huang, X., Xiu, J., Liu, C.: SocialRank: social network influence ranking method. In: 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems, vol. 2, pp. 591\u2013595. IEEE (2012)","DOI":"10.1109\/CCIS.2012.6664243"},{"key":"18_CR61","doi-asserted-by":"crossref","unstructured":"Tian, H., Xu, N., Liu, A.A., Zhang, Y.: Part-aware interactive learning for scene graph generation. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 3155\u20133163 (2020)","DOI":"10.1145\/3394171.3413501"},{"key":"18_CR62","doi-asserted-by":"crossref","unstructured":"Lin, X., Ding, C., Zeng, J., Tao, D.: GPS-Net: graph property sensing network for scene graph generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3746\u20133753 (2020)","DOI":"10.1109\/CVPR42600.2020.00380"},{"key":"18_CR63","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1007\/978-3-030-58592-1_36","volume-title":"Computer Vision \u2013 ECCV 2020","author":"A Zareian","year":"2020","unstructured":"Zareian, A., Karaman, S., Chang, S.-F.: Bridging knowledge graphs to generate scene graphs. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12368, pp. 606\u2013623. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58592-1_36"},{"key":"18_CR64","unstructured":"Wang, T.J.J., Pehlivan, S., Laaksonen, J.: Tackling the unannotated: scene graph generation with bias-reduced models. arXiv preprint arXiv:2008.07832 (2020)"},{"key":"18_CR65","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 (2021)","DOI":"10.1109\/CVPR46437.2021.01372"},{"key":"18_CR66","unstructured":"Tang, K.: A scene graph generation codebase in PyTorch (2020). https:\/\/github.com\/KaihuaTang\/Scene-Graph-Benchmark.pytorch"},{"key":"18_CR67","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"18_CR68","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492\u20131500 (2017)","DOI":"10.1109\/CVPR.2017.634"},{"key":"18_CR69","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26348-4_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T07:13:38Z","timestamp":1678259618000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26348-4_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263477","9783031263484"],"references-count":69,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26348-4_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Macao","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.accv2022.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT Microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"836","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"277","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"For the ACCV 2022 workshops 25 papers have been accepted from 40 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}