iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.crossref.org/works/10.1016/J.ESWA.2023.123022
{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,7]],"date-time":"2024-07-07T02:04:43Z","timestamp":1720317883709},"reference-count":50,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100019014","name":"Chengdu Municipal Science and Technology Program","doi-asserted-by":"publisher","award":["2022-YF05-02014-SN"],"id":[{"id":"10.13039\/501100019014","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Province Science and Technology Support Program","doi-asserted-by":"publisher","award":["2021JDRC0005"],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018AAA0103203"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1016\/j.eswa.2023.123022","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T11:58:05Z","timestamp":1703678285000},"page":"123022","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A joint learning method with consistency-aware for low-resolution facial expression recognition"],"prefix":"10.1016","volume":"244","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-9682-2065","authenticated-orcid":false,"given":"Yuanlun","family":"Xie","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5551-9796","authenticated-orcid":false,"given":"Wenhong","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1802-5188","authenticated-orcid":false,"given":"Ruini","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Zhiyuan","family":"Zha","sequence":"additional","affiliation":[]},{"given":"Bihan","family":"Wen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2023.123022_b1","doi-asserted-by":"crossref","unstructured":"Barsoum, E., Zhang, C., Ferrer, C. C., & Zhang, Z. (2016). Training deep networks for facial expression recognition with crowd-sourced label distribution. In Proceedings of the 18th ACM international conference on multimodal interaction (pp. 279\u2013283).","DOI":"10.1145\/2993148.2993165"},{"key":"10.1016\/j.eswa.2023.123022_b2","series-title":"2017 Seventh international conference on affective computing and intelligent interaction","first-page":"65","article-title":"Robust emotion recognition from low quality and low bit rate video: A deep learning approach","author":"Cheng","year":"2017"},{"key":"10.1016\/j.eswa.2023.123022_b3","series-title":"Computer vision\u2013ECCV 2014: 13th European conference, Zurich, Switzerland, September 6-12, 2014, proceedings, Part IV 13","first-page":"184","article-title":"Learning a deep convolutional network for image super-resolution","author":"Dong","year":"2014"},{"key":"10.1016\/j.eswa.2023.123022_b4","series-title":"Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, the Netherlands, October 11-14, 2016, proceedings, Part II 14","first-page":"391","article-title":"Accelerating the super-resolution convolutional neural network","author":"Dong","year":"2016"},{"key":"10.1016\/j.eswa.2023.123022_b5","doi-asserted-by":"crossref","unstructured":"Farzaneh, A. H., & Qi, X. (2021). Facial expression recognition in the wild via deep attentive center loss. In Proceedings of the IEEE\/CVF winter conference on applications of computer vision (pp. 2402\u20132411).","DOI":"10.1109\/WACV48630.2021.00245"},{"issue":"11","key":"10.1016\/j.eswa.2023.123022_b6","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.eswa.2023.123022_b7","doi-asserted-by":"crossref","unstructured":"Guo, H., Zheng, K., Fan, X., Yu, H., & Wang, S. (2019). Visual attention consistency under image transforms for multi-label image classification. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 729\u2013739).","DOI":"10.1109\/CVPR.2019.00082"},{"key":"10.1016\/j.eswa.2023.123022_b8","series-title":"Neural information processing: 28th International conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8\u201312, 2021, Proceedings, Part V 28","first-page":"387","article-title":"Task-driven super resolution: Object detection in low-resolution images","author":"Haris","year":"2021"},{"key":"10.1016\/j.eswa.2023.123022_b9","doi-asserted-by":"crossref","unstructured":"Hasani, B., & Mahoor, M. H. (2017). Facial expression recognition using enhanced deep 3D convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 30\u201340).","DOI":"10.1109\/CVPRW.2017.282"},{"key":"10.1016\/j.eswa.2023.123022_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.imu.2020.100372","article-title":"Development of a real-time emotion recognition system using facial expressions and EEG based on machine learning and deep neural network methods","volume":"20","author":"Hassouneh","year":"2020","journal-title":"Informatics in Medicine Unlocked"},{"key":"10.1016\/j.eswa.2023.123022_b11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770\u2013778).","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.eswa.2023.123022_b12","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ins.2021.08.043","article-title":"Facial expression recognition with grid-wise attention and visual transformer","volume":"580","author":"Huang","year":"2021","journal-title":"Information Sciences"},{"issue":"10","key":"10.1016\/j.eswa.2023.123022_b13","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1016\/j.patrec.2013.03.022","article-title":"Framework for reliable, real-time facial expression recognition for low resolution images","volume":"34","author":"Khan","year":"2013","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.eswa.2023.123022_b14","doi-asserted-by":"crossref","unstructured":"Kim, J., Lee, J. K., & Lee, K. M. (2016). Accurate image super-resolution using very deep convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1646\u20131654).","DOI":"10.1109\/CVPR.2016.182"},{"key":"10.1016\/j.eswa.2023.123022_b15","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Cunningham, A., Acosta, A., et al. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681\u20134690).","DOI":"10.1109\/CVPR.2017.19"},{"issue":"3","key":"10.1016\/j.eswa.2023.123022_b16","doi-asserted-by":"crossref","first-page":"1195","DOI":"10.1109\/TAFFC.2020.2981446","article-title":"Deep facial expression recognition: A survey","volume":"13","author":"Li","year":"2020","journal-title":"IEEE Transactions on Affective Computing"},{"key":"10.1016\/j.eswa.2023.123022_b17","doi-asserted-by":"crossref","unstructured":"Li, S., Deng, W., & Du, J. (2017). Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2852\u20132861).","DOI":"10.1109\/CVPR.2017.277"},{"key":"10.1016\/j.eswa.2023.123022_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120469","article-title":"MMFN: Emotion recognition by fusing touch gesture and facial expression information","volume":"228","author":"Li","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2023.123022_b19","doi-asserted-by":"crossref","first-page":"2016","DOI":"10.1109\/TIP.2021.3049955","article-title":"Adaptively learning facial expression representation via cf labels and distillation","volume":"30","author":"Li","year":"2021","journal-title":"IEEE Transactions on Image Processing"},{"issue":"5","key":"10.1016\/j.eswa.2023.123022_b20","doi-asserted-by":"crossref","first-page":"2439","DOI":"10.1109\/TIP.2018.2886767","article-title":"Occlusion aware facial expression recognition using CNN with attention mechanism","volume":"28","author":"Li","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.eswa.2023.123022_b21","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neucom.2019.09.035","article-title":"Effective image super resolution via hierarchical convolutional neural network","volume":"374","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2023.123022_b22","series-title":"MultiMedia modeling: 26th International conference, MMM 2020, Daejeon, South Korea, January 5\u20138, 2020, proceedings, Part II 26","first-page":"527","article-title":"Facial expression restoration based on improved graph convolutional networks","author":"Liu","year":"2020"},{"key":"10.1016\/j.eswa.2023.123022_b23","article-title":"Modeling uncertainty for low-resolution facial expression recognition","author":"Lo","year":"2023","journal-title":"IEEE Transactions on Affective Computing"},{"key":"10.1016\/j.eswa.2023.123022_b24","first-page":"1","article-title":"Facial expression recognition with visual transformers and attentional selective fusion","author":"Ma","year":"2021","journal-title":"IEEE Transactions on Affective Computing"},{"issue":"11","key":"10.1016\/j.eswa.2023.123022_b25","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2023.123022_b26","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107678","article-title":"Feature super-resolution based facial expression recognition for multi-scale low-resolution images","volume":"236","author":"Nan","year":"2022","journal-title":"Knowledge-Based Systems"},{"issue":"9","key":"10.1016\/j.eswa.2023.123022_b27","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.3390\/rs12091432","article-title":"Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network","volume":"12","author":"Rabbi","year":"2020","journal-title":"Remote Sensing"},{"key":"10.1016\/j.eswa.2023.123022_b28","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2022.3204461","article-title":"Image super-resolution via iterative refinement","author":"Saharia","year":"2022","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.eswa.2023.123022_b29","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s10489-020-01855-5","article-title":"E-FCNN for tiny facial expression recognition","volume":"51","author":"Shao","year":"2021","journal-title":"Applied Intelligence"},{"key":"10.1016\/j.eswa.2023.123022_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.115780","article-title":"An efficient image super resolution model with dense skip connections between complex filter structures in generative adversarial networks","volume":"186","author":"Sharma","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2023.123022_b31","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"6248","article-title":"Dive into ambiguity: Latent distribution mining and pairwise uncertainty estimation for facial expression recognition","author":"She","year":"2021"},{"key":"10.1016\/j.eswa.2023.123022_b32","series-title":"2020 IEEE 2nd international conference on civil aviation safety and information technology","first-page":"289","article-title":"Low-resolution facial expression recognition based on texture mapping-based gcforest","author":"Shen","year":"2020"},{"issue":"12","key":"10.1016\/j.eswa.2023.123022_b33","doi-asserted-by":"crossref","first-page":"8476","DOI":"10.1109\/TCSVT.2022.3195783","article-title":"Criteria comparative learning for real-scene image super-resolution","volume":"32","author":"Shi","year":"2022","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.eswa.2023.123022_b34","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2021.12.004","article-title":"Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network","volume":"82","author":"Tang","year":"2022","journal-title":"Information Fusion"},{"key":"10.1016\/j.eswa.2023.123022_b35","series-title":"Handbook of face recognition","first-page":"487","article-title":"Facial expression recognition","author":"Tian","year":"2011"},{"key":"10.1016\/j.eswa.2023.123022_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119957","article-title":"Efficient neural architecture search for emotion recognition","volume":"224","author":"Verma","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2023.123022_b37","doi-asserted-by":"crossref","unstructured":"Wang, Z., Chang, S., Yang, Y., Liu, D., & Huang, T. S. (2016). Studying very low resolution recognition using deep networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4792\u20134800).","DOI":"10.1109\/CVPR.2016.518"},{"key":"10.1016\/j.eswa.2023.123022_b38","doi-asserted-by":"crossref","unstructured":"Wang, K., Peng, X., Yang, J., Lu, S., & Qiao, Y. (2020a). Suppressing uncertainties for large-scale facial expression recognition. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 6897\u20136906).","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"10.1016\/j.eswa.2023.123022_b39","doi-asserted-by":"crossref","unstructured":"Wang, K., Peng, X., Yang, J., Lu, S., & Qiao, Y. (2020b). Suppressing Uncertainties for Large-Scale Facial Expression Recognition. In 2020 IEEE\/CVF conference on computer vision and pattern recognition (pp. 6896\u20136905).","DOI":"10.1109\/CVPR42600.2020.00693"},{"key":"10.1016\/j.eswa.2023.123022_b40","doi-asserted-by":"crossref","first-page":"4057","DOI":"10.1109\/TIP.2019.2956143","article-title":"Region attention networks for pose and occlusion robust facial expression recognition","volume":"29","author":"Wang","year":"2020","journal-title":"IEEE Transactions on Image Processing"},{"key":"10.1016\/j.eswa.2023.123022_b41","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., et al. (2018). Esrgan: Enhanced super-resolution generative adversarial networks. In Proceedings of the european conference on computer vision (ECCV) workshops.","DOI":"10.1007\/978-3-030-11021-5_5"},{"key":"10.1016\/j.eswa.2023.123022_b42","doi-asserted-by":"crossref","DOI":"10.1016\/j.sigpro.2019.107370","article-title":"Low-resolution facial expression recognition: A filter learning perspective","volume":"169","author":"Yan","year":"2020","journal-title":"Signal Processing"},{"key":"10.1016\/j.eswa.2023.123022_b43","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2019.112854","article-title":"Low resolution face recognition using a two-branch deep convolutional neural network architecture","volume":"139","author":"Zangeneh","year":"2020","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"10.1016\/j.eswa.2023.123022_b44","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1109\/TCSVT.2021.3071191","article-title":"A two-stage attentive network for single image super-resolution","volume":"32","author":"Zhang","year":"2021","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"10.1016\/j.eswa.2023.123022_b45","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018). Residual dense network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2472\u20132481).","DOI":"10.1109\/CVPR.2018.00262"},{"key":"10.1016\/j.eswa.2023.123022_b46","first-page":"17616","article-title":"Relative uncertainty learning for facial expression recognition","volume":"34","author":"Zhang","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2023.123022_b47","series-title":"Computer Vision\u2013ECCV 2022: 17th European conference, Tel Aviv, Israel, October 23\u201327, 2022, proceedings, Part XXVI","first-page":"418","article-title":"Learn from all: Erasing attention consistency for noisy label facial expression recognition","author":"Zhang","year":"2022"},{"key":"10.1016\/j.eswa.2023.123022_b48","first-page":"3510","article-title":"Robust lightweight facial expression recognition network with label distribution training","volume":"vol. 35","author":"Zhao","year":"2021"},{"key":"10.1016\/j.eswa.2023.123022_b49","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2016). Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2921\u20132929).","DOI":"10.1109\/CVPR.2016.319"},{"key":"10.1016\/j.eswa.2023.123022_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.116046","article-title":"Convolutional relation network for facial expression recognition in the wild with few-shot learning","volume":"189","author":"Zhu","year":"2022","journal-title":"Expert Systems with Applications"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417423035248?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417423035248?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T06:58:09Z","timestamp":1709794689000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417423035248"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":50,"alternative-id":["S0957417423035248"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.eswa.2023.123022","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A joint learning method with consistency-aware for low-resolution facial expression recognition","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2023.123022","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"123022"}}