{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T20:48:50Z","timestamp":1792183730696,"version":"4.4.0"},"reference-count":26,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"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":["Computers & Geosciences"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.cageo.2026.106228","type":"journal-article","created":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T21:56:13Z","timestamp":1783288573000},"page":"106228","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["Robust multimodal mineral identification based on a parameter-efficient method"],"prefix":"10.1016","volume":"215","author":[{"given":"Hongxiang","family":"Liao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6897-6606","authenticated-orcid":false,"given":"Xiaohui","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guocheng","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxue","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cageo.2026.106228_bib1","series-title":"Proceedings of the 5th International Conference on Learning Representations, ICLR 2017","article-title":"Gated multimodal units for information fusion","author":"Arevalo","year":"2017"},{"key":"10.1016\/j.cageo.2026.106228_bib2","article-title":"MiNet: A Convolutional Neural Network for Identifying and Categorising Minerals","author":"Brempong","year":"2021","journal-title":"arXiv preprint ArXiv, 2111.11260"},{"key":"10.1016\/j.cageo.2026.106228_bib3","article-title":"An image is worth 16x16 words: transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv preprint arXiv, 2010.11929"},{"key":"10.1016\/j.cageo.2026.106228_bib4","series-title":"Proceedings of 2024 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","first-page":"7681","article-title":"Dynamic multimodal information bottleneck for multimodality classification","author":"Fang","year":"2024"},{"key":"10.1016\/j.cageo.2026.106228_bib5","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.cageo.2026.106228_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2024.105532","article-title":"SwinMin: a mineral recognition model incorporating convolution and multi-scale contexts into swin transformer","volume":"184","author":"Jia","year":"2024","journal-title":"Comput. Geosci."},{"issue":"7","key":"10.1016\/j.cageo.2026.106228_bib7","first-page":"1266","article-title":"Intelligent recognition of mineral images based on an improved ConvNeXt network","volume":"43","author":"Li","year":"2024","journal-title":"Geol. Bull. China"},{"issue":"2","key":"10.1016\/j.cageo.2026.106228_bib8","first-page":"203","article-title":"A deep learning and intelligent recognition method of image data for rock mineral and its implementation","volume":"44","author":"Li","year":"2020","journal-title":"Geotect. Metallogenia"},{"key":"10.1016\/j.cageo.2026.106228_bib9","doi-asserted-by":"crossref","first-page":"109","DOI":"10.52202\/068431-0009","article-title":"Scaling & shifting your features: a new baseline for efficient model tuning","volume":"35","author":"Lian","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cageo.2026.106228_bib10","article-title":"Mineral identification based on multimodal and knowledge distillation","author":"Liao","year":"2025","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.cageo.2026.106228_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2020.104727","article-title":"Review of mineral recognition and its future","volume":"122","author":"Lou","year":"2020","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.cageo.2026.106228_bib12","article-title":"Uncertainty-encoded multi-modal fusion for robust object detection in autonomous driving","author":"Lou","year":"2023","journal-title":"arXiv preprint arXiv:2307.16121"},{"key":"10.1016\/j.cageo.2026.106228_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2023.105414","article-title":"MineralImage5k: a benchmark for zero-shot raw mineral visual recognition and description","volume":"178","author":"Nesteruk","year":"2023","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2026.106228_bib14","series-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems","first-page":"8026","article-title":"Pytorch: an imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"issue":"12","key":"10.1016\/j.cageo.2026.106228_bib15","first-page":"2059","article-title":"Common mineral intelligent recognition based on improved InceptionV3","volume":"38","author":"Peng","year":"2019","journal-title":"Geol. Bull. China"},{"issue":"2","key":"10.1016\/j.cageo.2026.106228_bib16","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1109\/TPAMI.2024.3476487","article-title":"Robust multimodal learning with missing modalities via parameter-efficient adaptation","volume":"47","author":"Reza","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"10.1016\/j.cageo.2026.106228_bib17","doi-asserted-by":"crossref","DOI":"10.1088\/1755-1315\/1032\/1\/012046","article-title":"Classifying minerals using deep learning algorithms","volume":"1032","author":"Singh","year":"2022","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"10.1016\/j.cageo.2026.106228_bib18","article-title":"The information bottleneck method","author":"Tishby","year":"2000","journal-title":"arXiv preprint physics\/0004057"},{"key":"10.1016\/j.cageo.2026.106228_bib19","series-title":"2015 IEEE Information Theory Workshop (Itw)","first-page":"1","article-title":"Deep learning and the information bottleneck principle","author":"Tishby","year":"2015"},{"issue":"4","key":"10.1016\/j.cageo.2026.106228_bib20","first-page":"112","article-title":"Mineral image recognition based on progressive multi-granularity training deep learning","volume":"31","author":"Wan","year":"2024","journal-title":"Earth Sci. Front."},{"issue":"4","key":"10.1016\/j.cageo.2026.106228_bib21","first-page":"87","article-title":"Mineral image recognition based on data augmentation and ensemble learning","volume":"31","author":"Wang","year":"2024","journal-title":"Earth Sci. Front."},{"issue":"6","key":"10.1016\/j.cageo.2026.106228_bib22","doi-asserted-by":"crossref","first-page":"3281","DOI":"10.2118\/220995-PA","article-title":"A hybrid tabular-spatial-temporal model with 3D geomodel for production prediction in shale gas formations","volume":"30","author":"Wang","year":"2025","journal-title":"SPE J."},{"key":"10.1016\/j.cageo.2026.106228_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.jgsce.2025.205617","article-title":"Integrating geological model via A multimodal machine learning approach in shale gas production forecast","volume":"139","author":"Wang","year":"2025","journal-title":"Gas Sci. Eng."},{"issue":"11","key":"10.1016\/j.cageo.2026.106228_bib24","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.3390\/min12111338","article-title":"Mineral identification based on multi-label image classification","volume":"12","author":"Wu","year":"2022","journal-title":"Minerals"},{"key":"10.1016\/j.cageo.2026.106228_bib25","article-title":"Deep multimodal learning with missing modality: a survey","author":"Wu","year":"2026","journal-title":"Transac. Machine Learn. Res."},{"issue":"5","key":"10.1016\/j.cageo.2026.106228_bib26","doi-asserted-by":"crossref","first-page":"506","DOI":"10.3390\/min11050506","article-title":"Mineral identification based on deep learning that combines image and mohs hardness","volume":"11","author":"Zeng","year":"2021","journal-title":"Minerals"}],"container-title":["Computers & Geosciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098300426001251?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098300426001251?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T08:10:13Z","timestamp":1783757413000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098300426001251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":26,"alternative-id":["S0098300426001251"],"URL":"https:\/\/doi.org\/10.1016\/j.cageo.2026.106228","relation":{},"ISSN":["0098-3004"],"issn-type":[{"value":"0098-3004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Robust multimodal mineral identification based on a parameter-efficient method","name":"articletitle","label":"Article Title"},{"value":"Computers & Geosciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cageo.2026.106228","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106228"}}