{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T15:05:43Z","timestamp":1768403143396,"version":"3.49.0"},"reference-count":75,"publisher":"Elsevier BV","issue":"1","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.elsevier.com\/tdm\/userlicense\/1.0\/"},{"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.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Processing &amp; Management"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1016\/j.ipm.2022.103106","type":"journal-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T12:39:29Z","timestamp":1666269569000},"page":"103106","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"title":["KBHN: A knowledge-aware bi-hypergraph network based on visual-knowledge features fusion for teaching image annotation"],"prefix":"10.1016","volume":"60","author":[{"given":"Hao","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1279-1845","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Du","sequence":"additional","affiliation":[]},{"given":"Zhuang","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Shuoqiu","family":"Yang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b1","article-title":"A review of methods for the image automatic annotation","author":"Adnan","year":"2021","journal-title":"Journal of Physics: Conference Series"},{"issue":"2","key":"10.1016\/j.ipm.2022.103106_b2","doi-asserted-by":"crossref","DOI":"10.19173\/irrodl.v20i2.4206","article-title":"Competency profile of the digital and online teacher in future education","volume":"20","author":"Ally","year":"2019","journal-title":"International Review of Research in Open and Distributed Learning"},{"issue":"4","key":"10.1016\/j.ipm.2022.103106_b3","doi-asserted-by":"crossref","first-page":"2393","DOI":"10.1007\/s10639-020-10201-8","article-title":"Self-regulated learning strategies in higher education: Fostering digital literacy for sustainable lifelong learning","volume":"25","author":"Anthonysamy","year":"2020","journal-title":"Education and Information Technologies"},{"key":"10.1016\/j.ipm.2022.103106_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.compedu.2021.104383","article-title":"Usage of augmented reality (AR) and development of e-learning outcomes: An empirical evaluation of students\u2019 e-learning experience","volume":"177","author":"Baabdullah","year":"2022","journal-title":"Computers & Education"},{"key":"10.1016\/j.ipm.2022.103106_b5","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2020.104204","article-title":"A crowdsourcing semi-automatic image segmentation platform for cell biology","volume":"130","author":"Bafti","year":"2021","journal-title":"Computers in Biology and Medicine"},{"key":"10.1016\/j.ipm.2022.103106_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107637","article-title":"Hypergraph convolution and hypergraph attention","volume":"110","author":"Bai","year":"2021","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ipm.2022.103106_b7","doi-asserted-by":"crossref","unstructured":"Berg,\u00a0A., Johnander,\u00a0J., Durand\u00a0de Gevigney,\u00a0F., Ahlberg,\u00a0J., & Felberg,\u00a0M. (2019). Semi-Automatic Annotation of Objects in Visual-Thermal Video. In 2019 IEEE\/CVF international conference on computer vision workshop (pp. 2242\u20132251).","DOI":"10.1109\/ICCVW.2019.00277"},{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b8","doi-asserted-by":"crossref","DOI":"10.1080\/2331186X.2018.1436123","article-title":"Active and traditional teaching, self-image, and motivation in learning math among pupils with learning disabilities","volume":"5","author":"Bishara","year":"2018","journal-title":"Cogent Education"},{"key":"10.1016\/j.ipm.2022.103106_b9","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"71","article-title":"Rosetta: Large scale system for text detection and recognition in images","author":"Borisyuk","year":"2018"},{"key":"10.1016\/j.ipm.2022.103106_b10","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.compedu.2019.04.008","article-title":"Investigating the use of innovative mobile pedagogies for school-aged students: A systematic literature review","volume":"138","author":"Burden","year":"2019","journal-title":"Computers & Education"},{"key":"10.1016\/j.ipm.2022.103106_b11","series-title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence (IJCAI)","first-page":"1923","article-title":"Hypergraph structure learning for hypergraph neural networks","author":"Cai","year":"2022"},{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b12","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","article-title":"Openpose: Realtime multi-person 2D pose estimation using part affinity fields","volume":"43","author":"Cao","year":"2021","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.ipm.2022.103106_b13","series-title":"2017 seventh international conference on image processing theory, tools and applications (IPTA)","first-page":"1","article-title":"Educational video classification by using a transcript to image transform and supervised learning","author":"Chatbri","year":"2017"},{"issue":"3","key":"10.1016\/j.ipm.2022.103106_b14","doi-asserted-by":"crossref","first-page":"4237","DOI":"10.1007\/s11042-020-09887-2","article-title":"The image annotation algorithm using convolutional features from intermediate layer of deep learning","volume":"80","author":"Chen","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"10.1016\/j.ipm.2022.103106_b15","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.patcog.2018.02.017","article-title":"A survey and analysis on automatic image annotation","volume":"79","author":"Cheng","year":"2018","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.ipm.2022.103106_b16","first-page":"741","article-title":"AI-human interactive pipeline with feedback to accelerate medical image annotation","volume":"12033","author":"Choi","year":"2022"},{"key":"10.1016\/j.ipm.2022.103106_b17","series-title":"Advances in neural information processing systems, Vol. 29","article-title":"R-FCN: Object detection via region-based fully convolutional networks","author":"Dai","year":"2016"},{"key":"10.1016\/j.ipm.2022.103106_b18","unstructured":"D\u2019Ascoli,\u00a0S., Touvron,\u00a0H., Leavitt,\u00a0M. L., Morcos,\u00a0A. S., Biroli,\u00a0G., & Sagun,\u00a0L. (2021). ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases. In Proceedings of the 38th international conference on machine learning, Vol. 139 (pp. 2286\u20132296)."},{"issue":"11","key":"10.1016\/j.ipm.2022.103106_b19","doi-asserted-by":"crossref","DOI":"10.3390\/info12110476","article-title":"Predicting student dropout in self-paced MOOC course using random forest model","volume":"12","author":"Dass","year":"2021","journal-title":"Information"},{"key":"10.1016\/j.ipm.2022.103106_b20","series-title":"2009 IEEE computer society conference on computer vision and pattern recognition workshops","first-page":"248","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"issue":"6","key":"10.1016\/j.ipm.2022.103106_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2020.102288","article-title":"Discriminative dual-stream deep hashing for large-scale image retrieval","volume":"57","author":"Ding","year":"2020","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2022.103106_b22","unstructured":"Dosovitskiy,\u00a0A., Beyer,\u00a0L., & Kolesnikov,\u00a0A. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In International conference on learning representations."},{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2021.102753","article-title":"Research of Chinese intangible cultural heritage knowledge graph construction and attribute value extraction with graph attention network","volume":"59","author":"Fan","year":"2022","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2022.103106_b24","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1109\/TIP.2019.2947792","article-title":"Combining Faster R-CNN and model-driven clustering for elongated object detection","volume":"29","author":"Fang","year":"2020","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.ipm.2022.103106_b25","doi-asserted-by":"crossref","unstructured":"Feng,\u00a0Y., You,\u00a0H., Zhang,\u00a0Z., Ji,\u00a0R., & Gao,\u00a0Y. (2019). Hypergraph Neural Networks. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33 (pp. 3558\u20133565).","DOI":"10.1609\/aaai.v33i01.33013558"},{"issue":"3","key":"10.1016\/j.ipm.2022.103106_b26","doi-asserted-by":"crossref","first-page":"89","DOI":"10.26417\/ejed.v1i3.p89-95","article-title":"The utilisation of images in the teaching of lessons","volume":"1","author":"Foutsitzi","year":"2018","journal-title":"European Journal of Education"},{"key":"10.1016\/j.ipm.2022.103106_b27","doi-asserted-by":"crossref","unstructured":"Girshick,\u00a0R. B., Donahue,\u00a0J., Darrell,\u00a0T., & Malik,\u00a0J. (2014). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. In 2014 IEEE conference on computer vision and pattern recognition (pp. 580\u2013587).","DOI":"10.1109\/CVPR.2014.81"},{"issue":"4","key":"10.1016\/j.ipm.2022.103106_b28","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1007\/s10462-021-10091-3","article-title":"Traditional to transfer learning progression on scene text detection and recognition: a survey","volume":"55","author":"Gupta","year":"2022","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.ipm.2022.103106_b29","series-title":"Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval","first-page":"2190","article-title":"DH-HGCN: Dual homogeneity hypergraph convolutional network for multiple social recommendations","author":"Han","year":"2022"},{"key":"10.1016\/j.ipm.2022.103106_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.micpro.2020.103343","article-title":"Ecological evolution path of smart education platform based on deep learning and image detection","volume":"80","author":"Han","year":"2021","journal-title":"Microprocessors and Microsystems"},{"key":"10.1016\/j.ipm.2022.103106_b31","doi-asserted-by":"crossref","unstructured":"He,\u00a0K., Zhang,\u00a0X., Ren,\u00a0S., & Sun,\u00a0J. (2016). Deep Residual Learning for Image Recognition. In 2016 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5","key":"10.1016\/j.ipm.2022.103106_b32","doi-asserted-by":"crossref","first-page":"6531","DOI":"10.1007\/s10639-021-10577-1","article-title":"Developing student connectedness under remote learning using digital resources: A systematic review","volume":"26","author":"Hehir","year":"2021","journal-title":"Education and Information Technologies"},{"key":"10.1016\/j.ipm.2022.103106_b33","series-title":"Advances in neural information processing systems, Vol. 32","article-title":"Gpipe: Efficient training of giant neural networks using pipeline parallelism","author":"Huang","year":"2019"},{"key":"10.1016\/j.ipm.2022.103106_b34","doi-asserted-by":"crossref","unstructured":"Huang,\u00a0J., Huang,\u00a0X., & Yang,\u00a0J. (2021). Residual Enhanced Multi-Hypergraph Neural Network. In 2021 IEEE international conference on image processing (ICIP) (pp. 3657\u20133661).","DOI":"10.1109\/ICIP42928.2021.9506153"},{"key":"10.1016\/j.ipm.2022.103106_b35","doi-asserted-by":"crossref","unstructured":"Huang,\u00a0G., Liu,\u00a0Z., Maaten,\u00a0L. V. D., & Weinberger,\u00a0K. Q. (2017). Densely Connected Convolutional Networks. In 2017 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2261\u20132269).","DOI":"10.1109\/CVPR.2017.243"},{"key":"10.1016\/j.ipm.2022.103106_b36","doi-asserted-by":"crossref","unstructured":"Ji,\u00a0S., Feng,\u00a0Y., Ji,\u00a0R., Zhao,\u00a0X., Tang,\u00a0W., & Gao,\u00a0Y. (2020). Dual Channel Hypergraph Collaborative Filtering. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 2020\u20132029).","DOI":"10.1145\/3394486.3403253"},{"key":"10.1016\/j.ipm.2022.103106_b37","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.ins.2022.07.041","article-title":"FC\u2013HAT: Hypergraph attention network for functional brain network classification","volume":"608","author":"Ji","year":"2022","journal-title":"Information Sciences"},{"key":"10.1016\/j.ipm.2022.103106_b38","doi-asserted-by":"crossref","unstructured":"Jiang,\u00a0J., Wei,\u00a0Y., Feng,\u00a0Y., Cao,\u00a0J., & Gao,\u00a0Y. (2019). Dynamic Hypergraph Neural Networks. In Proceedings of the twenty-eighth international joint conference on artificial intelligence, (IJCAI) (pp. 2635\u20132641).","DOI":"10.24963\/ijcai.2019\/366"},{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b39","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s11263-016-0981-7","article-title":"Visual genome: Connecting language and vision using crowdsourced dense image annotations","volume":"123","author":"Krishna","year":"2017","journal-title":"International Journal of Computer Vision"},{"key":"10.1016\/j.ipm.2022.103106_b40","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b41","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1007\/s10489-020-01827-9","article-title":"Deep learning and control algorithms of direct perception for autonomous driving","volume":"51","author":"Lee","year":"2021","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.ipm.2022.103106_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108785","article-title":"HAM: Hybrid attention module in deep convolutional neural networks for image classification","volume":"129","author":"Li","year":"2022","journal-title":"Pattern Recognition"},{"issue":"4","key":"10.1016\/j.ipm.2022.103106_b43","first-page":"985","article-title":"Scale-aware fast R-CNN for pedestrian detection","volume":"20","author":"Li","year":"2018","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.1016\/j.ipm.2022.103106_b44","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.knosys.2018.11.001","article-title":"A new pattern classification improvement method with local quality matrix based on K-NN","volume":"164","author":"Liu","year":"2019","journal-title":"Knowledge-Based Systems"},{"issue":"7","key":"10.1016\/j.ipm.2022.103106_b45","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1109\/TCYB.2018.2810806","article-title":"Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image","volume":"49","author":"Luo","year":"2019","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.1016\/j.ipm.2022.103106_b46","doi-asserted-by":"crossref","unstructured":"Mac\u00a0Aodha,\u00a0O., Su,\u00a0S., Chen,\u00a0Y., Perona,\u00a0P., & Yue,\u00a0Y. (2018). Teaching Categories to Human Learners With Visual Explanations. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) (pp. 3820\u20133828).","DOI":"10.1109\/CVPR.2018.00402"},{"key":"10.1016\/j.ipm.2022.103106_b47","series-title":"European conference on information retrieval","first-page":"289","article-title":"Slideimages: a dataset for educational image classification","author":"Morris","year":"2020"},{"issue":"2","key":"10.1016\/j.ipm.2022.103106_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.102865","article-title":"Predictive intelligence in evaluation of visual perception thresholds for visual pattern recognition and understanding","volume":"59","author":"Ogiela","year":"2022","journal-title":"Information Processing & Management"},{"issue":"15","key":"10.1016\/j.ipm.2022.103106_b49","doi-asserted-by":"crossref","first-page":"16389","DOI":"10.1007\/s11042-016-3918-9","article-title":"Local and global approaches for unsupervised image annotation","volume":"76","author":"Pellegrin","year":"2017","journal-title":"Multimedia Tools and Applications"},{"key":"10.1016\/j.ipm.2022.103106_b50","doi-asserted-by":"crossref","unstructured":"Pennington,\u00a0J., Socher,\u00a0R., & Manning,\u00a0C. (2014). GloVe: Global Vectors for Word Representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532\u20131543).","DOI":"10.3115\/v1\/D14-1162"},{"key":"10.1016\/j.ipm.2022.103106_b51","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117674","article-title":"Deep multi-similarity hashing with semantic-aware preservation for multi-label image retrieval","volume":"205","author":"Qin","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"06","key":"10.1016\/j.ipm.2022.103106_b52","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.ipm.2022.103106_b53","doi-asserted-by":"crossref","unstructured":"Sawhney,\u00a0R., Agarwal,\u00a0S., Wadhwa,\u00a0A., Derr,\u00a0T., & Shah,\u00a0R. R. (2021). Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach. In Thirty-fifth AAAI conference on artificial intelligence (AAAI) (pp. 497\u2013504).","DOI":"10.1609\/aaai.v35i1.16127"},{"key":"10.1016\/j.ipm.2022.103106_b54","doi-asserted-by":"crossref","DOI":"10.1016\/j.compmedimag.2019.101663","article-title":"Hypergraph based multi-task feature selection for multimodal classification of Alzheimer\u2019s disease","volume":"80","author":"Shao","year":"2020","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"2","key":"10.1016\/j.ipm.2022.103106_b55","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1109\/TPC.2022.3156225","article-title":"Understanding the effects of visual cueing on social media engagement with YouTube educational videos","volume":"65","author":"Shen","year":"2022","journal-title":"IEEE Transactions on Professional Communication"},{"key":"10.1016\/j.ipm.2022.103106_b56","doi-asserted-by":"crossref","DOI":"10.1155\/2022\/4260543","article-title":"A new multiface target detection algorithm for students in class based on Bayesian optimized YOLOv3 model","volume":"2022","author":"Shi","year":"2022","journal-title":"Journal of Electrical and Computer Engineering"},{"key":"10.1016\/j.ipm.2022.103106_b57","unstructured":"Simonyan,\u00a0K., & Zisserman,\u00a0A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd international conference on learning representations (ICLR)."},{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b58","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.ipm.2022.103106_b59","doi-asserted-by":"crossref","unstructured":"Szegedy,\u00a0C., Liu,\u00a0W., Jia,\u00a0Y., Sermanet,\u00a0P., & Reed,\u00a0S. (2015). Going deeper with convolutions. In 2015 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1\u20139).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"10.1016\/j.ipm.2022.103106_b60","series-title":"Proceedings of the 36th international conference on machine learning, Vol. 97","first-page":"6105","article-title":"Efficientnet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.ipm.2022.103106_b61","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"3","key":"10.1016\/j.ipm.2022.103106_b62","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2022.102938","article-title":"HGNN: Hyperedge-based graph neural network for MOOC course recommendation","volume":"59","author":"Wang","year":"2022","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.ipm.2022.103106_b63","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","first-page":"1306","article-title":"Improving OCR-based image captioning by incorporating geometrical relationship","author":"Wang","year":"2021"},{"issue":"3","key":"10.1016\/j.ipm.2022.103106_b64","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6404\/ab7a7f","article-title":"The benefit of computational modelling in physics teaching: a historical overview","volume":"41","author":"Weber","year":"2020","journal-title":"European Journal of Physics"},{"key":"10.1016\/j.ipm.2022.103106_b65","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.future.2022.05.014","article-title":"A survey of human-in-the-loop for machine learning","volume":"135","author":"Wu","year":"2022","journal-title":"Future Generation Computer Systems"},{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b66","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.jksuci.2017.08.002","article-title":"Swarm intelligence-based approach for educational data classification","volume":"31","author":"Yahya","year":"2019","journal-title":"Journal of King Saud University-Computer and Information Sciences"},{"issue":"2","key":"10.1016\/j.ipm.2022.103106_b67","doi-asserted-by":"crossref","DOI":"10.3390\/rs14020295","article-title":"Comparison of classical methods and mask R-CNN for automatic tree detection and mapping using UAV imagery","volume":"14","author":"Yu","year":"2022","journal-title":"Remote Sensing"},{"key":"10.1016\/j.ipm.2022.103106_b68","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2022.103514","article-title":"A survey of modern deep learning based object detection models","volume":"126","author":"Zaidi","year":"2022","journal-title":"Digital Signal Processing"},{"key":"10.1016\/j.ipm.2022.103106_b69","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116796","article-title":"Complex graph convolutional network for link prediction in knowledge graphs","volume":"200","author":"Zeb","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.ipm.2022.103106_b70","first-page":"1805","article-title":"Innovation of english teaching model based on machine learning neural network and image super resolution","volume":"39","author":"Zhang","year":"2020","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.1016\/j.ipm.2022.103106_b71","doi-asserted-by":"crossref","unstructured":"Zhang,\u00a0Z., Lin,\u00a0H., & Gao,\u00a0Y. (2018). Dynamic Hypergraph Structure Learning. In Proceedings of the twenty-seventh international joint conference on artificial intelligence (IJCAI) (pp. 3162\u20133169).","DOI":"10.24963\/ijcai.2018\/439"},{"issue":"12","key":"10.1016\/j.ipm.2022.103106_b72","doi-asserted-by":"crossref","first-page":"5957","DOI":"10.1109\/TIP.2018.2862625","article-title":"Inductive multi-hypergraph learning and its application on view-based 3D object classification","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.ipm.2022.103106_b73","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.knosys.2014.12.014","article-title":"Automatic image annotation via compact graph based semi-supervised learning","volume":"76","author":"Zhao","year":"2015","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"10.1016\/j.ipm.2022.103106_b74","doi-asserted-by":"crossref","DOI":"10.3390\/rs14010180","article-title":"SAR target detection based on improved SSD with saliency map and residual network","volume":"14","author":"Zhou","year":"2022","journal-title":"Remote Sensing"},{"key":"10.1016\/j.ipm.2022.103106_b75","series-title":"Advances in neural information processing systems, Vol. 19","article-title":"Learning with hypergraphs: Clustering, classification, and embedding","author":"Zhou","year":"2006"}],"container-title":["Information Processing &amp; Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457322002072?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0306457322002072?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T18:22:18Z","timestamp":1759688538000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457322002072"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":75,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["S0306457322002072"],"URL":"https:\/\/doi.org\/10.1016\/j.ipm.2022.103106","relation":{},"ISSN":["0306-4573"],"issn-type":[{"value":"0306-4573","type":"print"}],"subject":[],"published":{"date-parts":[[2023,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"KBHN: A knowledge-aware bi-hypergraph network based on visual-knowledge features fusion for teaching image annotation","name":"articletitle","label":"Article Title"},{"value":"Information Processing & Management","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ipm.2022.103106","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"103106"}}