{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T12:14:00Z","timestamp":1775132040015,"version":"3.50.1"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"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":["Multimedia Systems"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00530-025-02151-3","type":"journal-article","created":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T12:57:07Z","timestamp":1772629027000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A multimodal deep learning framework for nutritional estimation and health-oriented recipe analysis"],"prefix":"10.1007","volume":"32","author":[{"given":"Andrea","family":"Morales-Garz\u00f3n","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro","family":"Qui\u00f1ones-Mu\u00f1oz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karel","family":"Guti\u00e9rrez-Batista","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria J.","family":"Martin-Bautista","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,4]]},"reference":[{"key":"2151_CR1","doi-asserted-by":"crossref","unstructured":"Achananuparp, P., Lim, E.-P., Abhishek, V.: Does journaling encourage healthier choices? analyzing healthy eating behaviors of food journalers, 35\u201344 (2018)","DOI":"10.1145\/3194658.3194663"},{"issue":"11\u201312","key":"2151_CR2","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1016\/j.mad.2013.10.002","volume":"134","author":"A Berendsen","year":"2013","unstructured":"Berendsen, A., Santoro, A., Pini, E., Cevenini, E., Ostan, R., Pietruszka, B., Rolf, K., Cano, N., Caille, A., Lyon-Belgy, N., et al.: A parallel randomized trial on the effect of a healthful diet on inflammageing and its consequences in european elderly people: design of the nu-age dietary intervention study. Mech. Ageing Dev. 134(11\u201312), 523\u2013530 (2013)","journal-title":"Mech. Ageing Dev."},{"issue":"13","key":"2151_CR3","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1001\/archinte.161.13.1581","volume":"161","author":"AE Field","year":"2001","unstructured":"Field, A.E., Coakley, E.H., Must, A., Spadano, J.L., Laird, N., Dietz, W.H., Rimm, E., Colditz, G.A.: Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch. Intern. Med. 161(13), 1581\u20131586 (2001)","journal-title":"Arch. Intern. Med."},{"issue":"1","key":"2151_CR4","first-page":"6613385","volume":"2021","author":"E G\u00f3mez-Apo","year":"2021","unstructured":"G\u00f3mez-Apo, E., Mondrag\u00f3n-Maya, A., Ferrari-D\u00edaz, M., Silva-Pereyra, J.: Structural brain changes associated with overweight and obesity. Journal of obesity 2021(1), 6613385 (2021)","journal-title":"Journal of obesity"},{"issue":"1","key":"2151_CR5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1353\/foc.2006.0004","volume":"16","author":"SR Daniels","year":"2006","unstructured":"Daniels, S.R.: The consequences of childhood overweight and obesity. Future Child. 16(1), 47\u201367 (2006)","journal-title":"Future Child."},{"key":"2151_CR6","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Donoso, C., S\u00e1nchez-Villegas, A., Mart\u00ednez-Gonz\u00e1lez, M.A., Gea, A., Mendon\u00e7a, R.d.D., Lahortiga-Ramos, F., Bes-Rastrollo, M.: Ultra-processed food consumption and the incidence of depression in a mediterranean cohort: the sun project. European journal of nutrition 59, 1093\u20131103 (2020)","DOI":"10.1007\/s00394-019-01970-1"},{"issue":"10","key":"2151_CR7","doi-asserted-by":"publisher","first-page":"2080","DOI":"10.1038\/s41366-020-00650-z","volume":"44","author":"M Askari","year":"2020","unstructured":"Askari, M., Heshmati, J., Shahinfar, H., Tripathi, N., Daneshzad, E.: Ultra-processed food and the risk of overweight and obesity: a systematic review and meta-analysis of observational studies. Int. J. Obes. 44(10), 2080\u20132091 (2020)","journal-title":"Int. J. Obes."},{"issue":"5","key":"2151_CR8","doi-asserted-by":"publisher","first-page":"1673","DOI":"10.1093\/advances\/nmab049","volume":"12","author":"F Juul","year":"2021","unstructured":"Juul, F., Vaidean, G., Parekh, N.: Ultra-processed foods and cardiovascular diseases: potential mechanisms of action. Adv. Nutr. 12(5), 1673\u20131680 (2021)","journal-title":"Adv. Nutr."},{"issue":"3","key":"2151_CR9","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.appet.2008.05.061","volume":"51","author":"G Ares","year":"2008","unstructured":"Ares, G., Gim\u00e9nez, A., G\u00e1mbaro, A.: Influence of nutritional knowledge on perceived healthiness and willingness to try functional foods. Appetite 51(3), 663\u2013668 (2008)","journal-title":"Appetite"},{"issue":"1","key":"2151_CR10","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.tjnut.2022.10.001","volume":"153","author":"C Diekman","year":"2023","unstructured":"Diekman, C., Ryan, C.D., Oliver, T.L.: Misinformation and disinformation in food science and nutrition: Impact on practice. J. Nutr. 153(1), 3\u20139 (2023)","journal-title":"J. Nutr."},{"key":"2151_CR11","doi-asserted-by":"crossref","unstructured":"Trattner, C., Elsweiler, D.: Investigating the healthiness of internet-sourced recipes: implications for meal planning and recommender systems. In: Proceedings of the 26th International Conference on World Wide Web, pp. 489\u2013498 (2017)","DOI":"10.1145\/3038912.3052573"},{"issue":"15","key":"2151_CR12","doi-asserted-by":"publisher","first-page":"4283","DOI":"10.3390\/s20154283","volume":"20","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Stathopoulou, T., Vasiloglou, M.F., Pinault, L.F., Kiley, C., Spanakis, E.K., Mougiakakou, S.: gofoodtm: an artificial intelligence system for dietary assessment. Sensors 20(15), 4283 (2020)","journal-title":"Sensors"},{"key":"2151_CR13","doi-asserted-by":"crossref","unstructured":"Ruede, R., Heusser, V., Frank, L., Roitberg, A., Haurilet, M., Stiefelhagen, R.: Multi-task learning for calorie prediction on a novel large-scale recipe dataset enriched with nutritional information, 4001\u20134008 (2021). IEEE","DOI":"10.1109\/ICPR48806.2021.9412839"},{"key":"2151_CR14","doi-asserted-by":"crossref","unstructured":"Thames, Q., Karpur, A., Norris, W., Xia, F., Panait, L., Weyand, T., Sim, J.: Nutrition5k: Towards automatic nutritional understanding of generic food. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8903\u20138911 (2021)","DOI":"10.1109\/CVPR46437.2021.00879"},{"issue":"10","key":"2151_CR15","doi-asserted-by":"publisher","first-page":"1811","DOI":"10.3390\/math8101811","volume":"8","author":"G Ispirova","year":"2020","unstructured":"Ispirova, G., Eftimov, T., Korou\u0161i\u0107 Seljak, B.: P-nut: Predicting nutrient content from short text descriptions. Mathematics 8(10), 1811 (2020)","journal-title":"Mathematics"},{"issue":"16","key":"2151_CR16","doi-asserted-by":"publisher","first-page":"1941","DOI":"10.3390\/math9161941","volume":"9","author":"G Ispirova","year":"2021","unstructured":"Ispirova, G., Eftimov, T., Korou\u0161i\u0107 Seljak, B.: Domain heuristic fusion of multi-word embeddings for nutrient value prediction. Mathematics 9(16), 1941 (2021)","journal-title":"Mathematics"},{"key":"2151_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.foodchem.2022.133243","volume":"391","author":"P Ma","year":"2022","unstructured":"Ma, P., Zhang, Z., Li, Y., Yu, N., Sheng, J., McGinty, H.K., Wang, Q., Ahuja, J.K.: Deep learning accurately predicts food categories and nutrients based on ingredient statements. Food Chem. 391, 133243 (2022)","journal-title":"Food Chem."},{"issue":"3","key":"2151_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00530-025-01809-2","volume":"31","author":"R Zhang","year":"2025","unstructured":"Zhang, R., Ouyang, D., Li, X., Bai, H., Zhang, C., He, L.: Learning multi-scale features automatically from food and ingredients. Multimedia Syst. 31(3), 1\u201311 (2025)","journal-title":"Multimedia Syst."},{"issue":"1","key":"2151_CR19","first-page":"1","volume":"31","author":"A Morales-Garz\u00f3n","year":"2025","unstructured":"Morales-Garz\u00f3n, A., Guti\u00e9rrez-Batista, K., Martin-Bautista, M.J.: Adaptafood: an intelligent system to adapt recipes to specialised diets and healthy lifestyles. Multimedia Syst. 31(1), 1\u201324 (2025)","journal-title":"Multimedia Syst."},{"key":"2151_CR20","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhang, Y., Liu, Z., Yuan, Y., Cheng, L., Zimmermann, R.: Multi-modal multi-task learning for automatic dietary assessment. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11848"},{"issue":"3","key":"2151_CR21","first-page":"3363","volume":"45","author":"H Wang","year":"2022","unstructured":"Wang, H., Lin, G., Hoi, S.C., Miao, C.: Learning structural representations for recipe generation and food retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3363\u20133377 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"2151_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3329168","volume":"52","author":"W Min","year":"2019","unstructured":"Min, W., Jiang, S., Liu, L., Rui, Y., Jain, R.: A survey on food computing. ACM Computing Surveys (CSUR) 52(5), 1\u201336 (2019)","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"2151_CR23","doi-asserted-by":"crossref","unstructured":"Okamoto, K., Yanai, K.: An automatic calorie estimation system of food images on a smartphone, 63\u201370 (2016)","DOI":"10.1145\/2986035.2986040"},{"issue":"2","key":"2151_CR24","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.compag.2010.01.001","volume":"71","author":"Y Huang","year":"2010","unstructured":"Huang, Y., Lan, Y., Thomson, S.J., Fang, A., Hoffmann, W.C., Lacey, R.E.: Development of soft computing and applications in agricultural and biological engineering. Comput. Electron. Agric. 71(2), 107\u2013127 (2010)","journal-title":"Comput. Electron. Agric."},{"issue":"6","key":"2151_CR25","doi-asserted-by":"publisher","first-page":"453","DOI":"10.2307\/1313553","volume":"49","author":"PW Sherman","year":"1999","unstructured":"Sherman, P.W., Billing, J.: Darwinian gastronomy: Why we use spices: Spices taste good because they are good for us. Bioscience 49(6), 453\u2013463 (1999)","journal-title":"Bioscience"},{"issue":"2\u20133","key":"2151_CR26","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/S0168-1699(02)00100-X","volume":"36","author":"Y-R Chen","year":"2002","unstructured":"Chen, Y.-R., Chao, K., Kim, M.S.: Machine vision technology for agricultural applications. Comput. Electron. Agric. 36(2\u20133), 173\u2013191 (2002)","journal-title":"Comput. Electron. Agric."},{"issue":"3","key":"2151_CR27","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s00530-024-01354-4","volume":"30","author":"Z Feng","year":"2024","unstructured":"Feng, Z., Li, X., Li, Y.: Mtkgr: multi-task knowledge graph reasoning for food and ingredient recognition. Multimedia Syst. 30(3), 149 (2024)","journal-title":"Multimedia Syst."},{"key":"2151_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127326","volume":"576","author":"M Rostami","year":"2024","unstructured":"Rostami, M., Berahmand, K., Forouzandeh, S., Ahmadian, S., Farrahi, V., Oussalah, M.: A novel healthy food recommendation to user groups based on a deep social community detection approach. Neurocomputing 576, 127326 (2024)","journal-title":"Neurocomputing"},{"key":"2151_CR29","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.cmpb.2017.10.014","volume":"153","author":"G Agapito","year":"2018","unstructured":"Agapito, G., Simeoni, M., Calabrese, B., Car\u00e9, I., Lamprinoudi, T., Guzzi, P.H., Pujia, A., Fuiano, G., Cannataro, M.: Dietos: A dietary recommender system for chronic diseases monitoring and management. Comput. Methods Programs Biomed. 153, 93\u2013104 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"2151_CR30","doi-asserted-by":"publisher","first-page":"2679","DOI":"10.2196\/jmir.2679","volume":"16","author":"D Capurro","year":"2014","unstructured":"Capurro, D., Cole, K., Echavarr\u00eda, M.I., Joe, J., Neogi, T., Turner, A.M., et al.: The use of social networking sites for public health practice and research: a systematic review. J. Med. Internet Res. 16(3), 2679 (2014)","journal-title":"J. Med. Internet Res."},{"key":"2151_CR31","doi-asserted-by":"crossref","unstructured":"Chokr, M., Elbassuoni, S.: Calories prediction from food images. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, pp. 4664\u20134669 (2017)","DOI":"10.1609\/aaai.v31i2.19092"},{"key":"2151_CR32","doi-asserted-by":"crossref","unstructured":"Min, W., Wang, Z., Liu, Y., Luo, M., Kang, L., Wei, X., Wei, X., Jiang, S.: Large scale visual food recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)","DOI":"10.1109\/TPAMI.2023.3237871"},{"key":"2151_CR33","doi-asserted-by":"crossref","unstructured":"Ege, T., Yanai, K.: Multi-task learning of dish detection and calorie estimation. In: Proceedings of the Joint Workshop on Multimedia for Cooking and Eating Activities and Multimedia Assisted Dietary Management, pp. 53\u201358 (2018)","DOI":"10.1145\/3230519.3230594"},{"issue":"4","key":"2151_CR34","doi-asserted-by":"publisher","first-page":"877","DOI":"10.3390\/nu11040877","volume":"11","author":"S Fang","year":"2019","unstructured":"Fang, S., Shao, Z., Kerr, D.A., Boushey, C.J., Zhu, F.: An end-to-end image-based automatic food energy estimation technique based on learned energy distribution images: Protocol and methodology. Nutrients 11(4), 877 (2019)","journal-title":"Nutrients"},{"key":"2151_CR35","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)"},{"key":"2151_CR36","doi-asserted-by":"publisher","first-page":"27389","DOI":"10.1109\/ACCESS.2021.3058559","volume":"9","author":"A Morales-Garz\u00f3n","year":"2021","unstructured":"Morales-Garz\u00f3n, A., G\u00f3mez-Romero, J., Martin-Bautista, M.J.: A word embedding-based method for unsupervised adaptation of cooking recipes. IEEE Access 9, 27389\u201327404 (2021)","journal-title":"IEEE Access"},{"key":"2151_CR37","doi-asserted-by":"crossref","unstructured":"Salvador, A., Hynes, N., Aytar, Y., Marin, J., Ofli, F., Weber, I., Torralba, A.: Learning cross-modal embeddings for cooking recipes and food images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3020\u20133028 (2017)","DOI":"10.1109\/CVPR.2017.327"},{"key":"2151_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108357","volume":"255","author":"C-K Lee","year":"2024","unstructured":"Lee, C.-K., Chen, T.-L., Wu, J.-E., Liao, M.-T., Wang, C., Wang, W., Chou, C.-Y.: Multimodal deep learning models utilizing chest x-ray and electronic health record data for predictive screening of acute heart failure in emergency department. Comput. Methods Programs Biomed. 255, 108357 (2024)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"4","key":"2151_CR39","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1007\/s42979-023-01870-6","volume":"4","author":"AFUR Khilji","year":"2023","unstructured":"Khilji, A.F.U.R., Sinha, U., Singh, P., Ali, A., Dadure, P., Manna, R., Pakray, P.: Multimodal recipe recommendation system using deep learning and rule-based approach. SN Computer Science 4(4), 421 (2023)","journal-title":"SN Computer Science"},{"key":"2151_CR40","doi-asserted-by":"crossref","unstructured":"Qin, J., Kim, M.S., Hong, J., Cho, H., Van\u00a0Kessel, J.A.S., Baek, I., Chao, K.: Development of a multimodal sensing system for automated and intelligent food safety inspection 12545, 18\u201324 (2023). SPIE","DOI":"10.1117\/12.2663710"},{"key":"2151_CR41","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al., Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763 (2021). PmLR"},{"key":"2151_CR42","unstructured":"Li, J., Li, D., Xiong, C., Hoi, S.: Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In: International Conference on Machine Learning, pp. 12888\u201312900 (2022). PMLR"},{"key":"2151_CR43","unstructured":"Kim, W., Son, B., Kim, I.: Vilt: Vision-and-language transformer without convolution or region supervision. In: International Conference on Machine Learning, pp. 5583\u20135594 (2021). PMLR"},{"key":"2151_CR44","doi-asserted-by":"crossref","unstructured":"Shukor, M., Couairon, G., Grechka, A., Cord, M.: Transformer decoders with multimodal regularization for cross-modal food retrieval. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4567\u20134578 (2022)","DOI":"10.1109\/CVPRW56347.2022.00503"},{"key":"2151_CR45","doi-asserted-by":"crossref","unstructured":"Xu, M., Wang, J., Tao, M., Bao, B.-K., Xu, C.: Cookgalip: Recipe controllable generative adversarial clips with sequential ingredient prompts for food image generation. IEEE Transactions on Multimedia (2024)","DOI":"10.1109\/TMM.2024.3377540"},{"key":"2151_CR46","doi-asserted-by":"crossref","unstructured":"Wijaya, C.R., Manuaba, I.B.K., Purwita, A.A.: Food text-to-image synthesis using vqgan and clip. In: 2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 53\u201358 (2023). IEEE","DOI":"10.1109\/ISRITI60336.2023.10467716"},{"issue":"11","key":"2151_CR47","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.3390\/agriculture15111173","volume":"15","author":"Y Zhang","year":"2025","unstructured":"Zhang, Y., Shao, Y., Tang, C., Liu, Z., Li, Z., Zhai, R., Peng, H., Song, P.: E-clip: An enhanced clip-based visual language model for fruit detection and recognition. Agriculture 15(11), 1173 (2025)","journal-title":"Agriculture"},{"issue":"8","key":"2151_CR48","doi-asserted-by":"publisher","first-page":"197","DOI":"10.3390\/computation13080197","volume":"13","author":"H Zhao","year":"2025","unstructured":"Zhao, H., Chen, H., Wang, J., Wang, Y.: Cross-view heterogeneous graph contrastive learning method for healthy food recommendation. Computation 13(8), 197 (2025)","journal-title":"Computation"},{"key":"2151_CR49","doi-asserted-by":"crossref","unstructured":"Jiang, F., Ye, Z., Zhou, L., Huang, J.: Text enhanced curriculum supervised contrastive learning for food image recognition. Neurocomputing, 131781 (2025)","DOI":"10.1016\/j.neucom.2025.131781"},{"key":"2151_CR50","doi-asserted-by":"crossref","unstructured":"Qi, H., Zhu, B., Ngo, C.-W., Chen, J., Lim, E.-P.: Advancing food nutrition estimation via visual-ingredient feature fusion. In: Proceedings of the 2025 International Conference on Multimedia Retrieval, pp. 1091\u20131099 (2025)","DOI":"10.1145\/3731715.3733269"},{"key":"2151_CR51","doi-asserted-by":"crossref","unstructured":"Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., Yang, J.: Pfid: Pittsburgh fast-food image dataset. In: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 289\u2013292 (2009). IEEE","DOI":"10.1109\/ICIP.2009.5413511"},{"key":"2151_CR52","doi-asserted-by":"crossref","unstructured":"Yin, Y., Qi, H., Zhu, B., Chen, J., Jiang, Y.-G., Ngo, C.-W.: Foodlmm: A versatile food assistant using large multi-modal model. IEEE Transactions on Multimedia (2025)","DOI":"10.1109\/TMM.2025.3590924"},{"issue":"6","key":"2151_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2019.05.012","volume":"57","author":"M Chen","year":"2020","unstructured":"Chen, M., Jia, X., Gorbonos, E., Hoang, C.T., Yu, X., Liu, Y.: Eating healthier: Exploring nutrition information for healthier recipe recommendation. Information Processing & Management 57(6), 102051 (2020)","journal-title":"Information Processing & Management"},{"key":"2151_CR54","doi-asserted-by":"crossref","unstructured":"Pecune, F., Callebert, L., Marsella, S.: A recommender system for healthy and personalized recipes recommendations. In: HealthRecSys@ RecSys, pp. 15\u201320 (2020)","DOI":"10.1145\/3406499.3415079"},{"key":"2151_CR55","doi-asserted-by":"crossref","unstructured":"Li, M., Li, L., Tao, X., Xie, Z., Xie, Q., Yuan, J.: Boosting healthiness exposure in category-constrained meal recommendation using nutritional standards. ACM Transactions on Intelligent Systems and Technology (2024)","DOI":"10.1145\/3643859"},{"key":"2151_CR56","doi-asserted-by":"crossref","unstructured":"Morales-Garz\u00f3n, A., Guti\u00e9rrez-Batista, K., Martin-Bautista, M.J.: Link prediction in food heterogeneous graphs for personalised recipe recommendation based on user interactions and dietary restrictions. Computing, 1\u201323 (2023)","DOI":"10.1007\/s00607-023-01233-2"},{"key":"2151_CR57","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks, 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"2151_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00444-8","volume":"8","author":"L Alzubaidi","year":"2021","unstructured":"Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamar\u00eda, J., Fadhel, M.A., Al-Amidie, M., Farhan, L.: Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions. Journal of big Data 8, 1\u201374 (2021)","journal-title":"Journal of big Data"},{"key":"2151_CR59","doi-asserted-by":"crossref","unstructured":"Kornblith, S., Shlens, J., Le, Q.V.: Do better imagenet models transfer better?, 2661\u20132671 (2019)","DOI":"10.1109\/CVPR.2019.00277"},{"key":"2151_CR60","doi-asserted-by":"crossref","unstructured":"Reimers, N., Gurevych, I.: Sentence-bert: Sentence embeddings using siamese bert-networks. arXiv:1908.10084 (2019)","DOI":"10.18653\/v1\/D19-1410"},{"key":"2151_CR61","doi-asserted-by":"crossref","unstructured":"Hossain, M.S., Hossain, M.M., Chaki, S., Mridha, M., Rahman, M.S., Moni, M.A.: Dimension-wise gated cross-attention for multimodal sentiment analysis. In: Companion Proceedings of the ACM on Web Conference 2025, pp. 1979\u20131987 (2025)","DOI":"10.1145\/3701716.3718381"},{"key":"2151_CR62","unstructured":"Jiang, M., Ji, S.: Cross-modality gated attention fusion for multimodal sentiment analysis. arXiv:2208.11893 (2022)"},{"issue":"1","key":"2151_CR63","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1093\/nutrit\/nuw044","volume":"75","author":"MJ S\u00e1nchez-Pe\u00f1a","year":"2017","unstructured":"S\u00e1nchez-Pe\u00f1a, M.J., M\u00e1rquez-Sandoval, F., Ram\u00edrez-Anguiano, A.C., Velasco-Ram\u00edrez, S.F., Macedo-Ojeda, G., Gonz\u00e1lez-Ortiz, L.J.: Calculating the metabolizable energy of macronutrients: a critical review of atwater\u2019s results. Nutr. Rev. 75(1), 37\u201348 (2017)","journal-title":"Nutr. Rev."},{"key":"2151_CR64","unstructured":"Marin, J., Biswas, A., Ofli, F., Hynes, N., Salvador, A., Aytar, Y., Weber, I., Torralba, A.: Recipe1m+: a dataset for learning cross-modal embeddings for cooking recipes and food images. arXiv:1810.06553 (2018)"},{"issue":"7","key":"2151_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3713070","volume":"57","author":"Y Yuan","year":"2025","unstructured":"Yuan, Y., Li, Z., Zhao, B.: A survey of multimodal learning: Methods, applications, and future. ACM Comput. Surv. 57(7), 1\u201334 (2025)","journal-title":"ACM Comput. Surv."},{"key":"2151_CR66","unstructured":"Wu, R., Wang, H., Chen, H.-T., Carneiro, G.: Deep multimodal learning with missing modality: A survey. arXiv:2409.07825 (2024)"},{"key":"2151_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2024.100720","volume":"56","author":"LP Le","year":"2025","unstructured":"Le, L.P., Nguyen, T., Riegler, M.A., Halvorsen, P., Nguyen, B.T.: Multimodal missing data in healthcare: A comprehensive review and future directions. Computer Science Review 56, 100720 (2025)","journal-title":"Computer Science Review"},{"key":"2151_CR68","doi-asserted-by":"crossref","unstructured":"Wahed, M., Zhou, X., Yu, T., Lourentzou, I.: Fine-grained alignment for cross-modal recipe retrieval. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 5584\u20135593 (2024)","DOI":"10.1109\/WACV57701.2024.00549"},{"key":"2151_CR69","doi-asserted-by":"crossref","unstructured":"Salvador, A., Gundogdu, E., Bazzani, L., Donoser, M.: Revamping cross-modal recipe retrieval with hierarchical transformers and self-supervised learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15475\u201315484 (2021)","DOI":"10.1109\/CVPR46437.2021.01522"},{"key":"2151_CR70","doi-asserted-by":"crossref","unstructured":"Jha, S., Garewal, I.K., Aathisaya, L., Alphonso, L., Aher, B.: Foodmo: A food nutrient analysis application using optical character recognition and machine learning. In: International Conference on ICT for Sustainable Development, pp. 589\u2013600 (2024). Springer","DOI":"10.1007\/978-981-97-8526-1_47"},{"key":"2151_CR71","doi-asserted-by":"crossref","unstructured":"Bie\u0144, M., Gilski, M., Maciejewska, M., Taisner, W., Wisniewski, D., Lawrynowicz, A.: Recipenlg: A cooking recipes dataset for semi-structured text generation. In: Proceedings of the 13th International Conference on Natural Language Generation, pp. 22\u201328 (2020)","DOI":"10.18653\/v1\/2020.inlg-1.4"},{"key":"2151_CR72","doi-asserted-by":"crossref","unstructured":"Alfasly, S., Lu, J., Xu, C., Zou, Y.: Learnable irrelevant modality dropout for multimodal action recognition on modality-specific annotated videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20208\u201320217 (2022)","DOI":"10.1109\/CVPR52688.2022.01957"},{"key":"2151_CR73","doi-asserted-by":"publisher","first-page":"1483131","DOI":"10.3389\/fnbot.2024.1483131","volume":"18","author":"N Wang","year":"2024","unstructured":"Wang, N.: Multimodal robot-assisted english writing guidance and error correction with reinforcement learning. Front. Neurorobot. 18, 1483131 (2024)","journal-title":"Front. Neurorobot."},{"issue":"3","key":"2151_CR74","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1093\/ptj\/85.3.257","volume":"85","author":"J Sim","year":"2005","unstructured":"Sim, J., Wright, C.C.: The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys. Ther. 85(3), 257\u2013268 (2005)","journal-title":"Phys. Ther."},{"key":"2151_CR75","unstructured":"Arrighi, L., Moraes, I.A., Zullich, M., Simonato, M., Barbin, D.F., Junior, S.B.: Explainable artificial intelligence techniques for interpretation of food datasets: a review. arXiv:2504.10527 (2025)"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02151-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-02151-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-02151-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T11:36:49Z","timestamp":1775129809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-02151-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"references-count":75,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["2151"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-02151-3","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]},"assertion":[{"value":"29 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"153"}}