{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T20:18:54Z","timestamp":1776889134150,"version":"3.51.2"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,6,4]],"date-time":"2023-06-04T00:00:00Z","timestamp":1685836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,4]]},"DOI":"10.1109\/icassp49357.2023.10095216","type":"proceedings-article","created":{"date-parts":[[2023,5,5]],"date-time":"2023-05-05T17:28:30Z","timestamp":1683307710000},"page":"1-5","source":"Crossref","is-referenced-by-count":1,"title":["Hierarchical Multi-Task Learning for Fabric Component Analysis Based on NIR Spectral Signals"],"prefix":"10.1109","author":[{"given":"Joseph","family":"Kim","sequence":"first","affiliation":[{"name":"Fudan University,School of Computer Science, Shanghai Key Llaboratory of Data Science,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Wu","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science, Shanghai Key Llaboratory of Data Science,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingmin","family":"Chi","sequence":"additional","affiliation":[{"name":"Fudan University,School of Computer Science, Shanghai Key Llaboratory of Data Science,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaoqi","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhongshan Fudan Joint Innovation Center,Zhongshan PoolNet Technology Co., Ltd,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7016"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i2.16176"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/math9233137"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"he","year":"2016","journal-title":"2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2996736"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3140531"},{"key":"ref11","first-page":"82","article-title":"Asymmetric loss for multi-label classification","author":"baruch","year":"2021","journal-title":"2021 IEEE\/CVF International Conference on Computer Vision (ICCV)"},{"key":"ref22","article-title":"Large batch optimization for deep learning: Training bert in 76 minutes","author":"you","year":"2020"},{"key":"ref10","article-title":"Bayes optimal multilabel classification via probabilistic classifier chains","author":"dembczynski","year":"2010","journal-title":"ICML"},{"key":"ref21","article-title":"The gatedtab-transformer. an enhanced deep learning architecture for tabular modeling","author":"cholakov","year":"2022","journal-title":"ArXiv"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1515\/aut-2018-0055"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.postharvbio.2007.06.024"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858826"},{"key":"ref16","article-title":"Mish: A self regularized nonmonotonic activation function","author":"misra","year":"2020","journal-title":"BMVC"},{"key":"ref19","article-title":"Pay attention to mlps","author":"liu","year":"2021","journal-title":"NeurIPS"},{"key":"ref18","article-title":"When does label smoothing help?","author":"m\u00fcller","year":"2019","journal-title":"NeurIPS"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1186\/s40494-019-0337-z"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/11573036_42"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1177\/0967033518757069"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1109\/TNN.2005.848998","article-title":"Learning with kernels: Support vector machines, regularization, optimization, and beyond","volume":"16","author":"atiya","year":"2005","journal-title":"IEEE Transactions on Neural Networks"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.matdes.2014.03.022"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.107965"}],"event":{"name":"ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","location":"Rhodes Island, Greece","start":{"date-parts":[[2023,6,4]]},"end":{"date-parts":[[2023,6,10]]}},"container-title":["ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10094559\/10094560\/10095216.pdf?arnumber=10095216","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T19:00:40Z","timestamp":1700506840000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10095216\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,4]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/icassp49357.2023.10095216","relation":{},"subject":[],"published":{"date-parts":[[2023,6,4]]}}}