资源受限场景下的少样本跨域文档检索模型

［1］全国数据资源调查工作组（国家工业信息安全发展研究中心）.全国数据资源调查报告（2023年）［R］.福州：第七届数字中国建设峰会·数据资源与数字安全论坛，2024.

［2］中国互联网络信息中心.第53次中国互联网络发展状况统计报告［R］.北京：中国互联网络信息中心，2024.

［3］WANG X,PENG D Z,HU P,et al.Cross-domain alignment for zero-shot sketch-based image retrieval［J］.IEEE Transactions on Circuits and Systems for Video Technology,2023,33(11)：7024-7035.

［4］ZHANG H X,CHENG D Q,KOU Q Q,et al.Indicative Vision Transformer for end-to-end zero-shot sketch-based image retrieval［J］.Advanced Engineering Informatics,2024,60：102398.

［5］WU L,WANG Y,SHAO L.Cycle-consistent deep generative hashing for cross-modal retrieval［J］.IEEE Transactions on Image Processing,2019,28(4)：1602-1612.

［6］廖颖.面向长文档的智能问答技术研究［D］.秦皇岛：燕山大学,2023.

［7］杨帆.基于语义增强特征融合的多模态图像检索模型［D］.大连：大连海事大学,2023.

［8］SUN Y,REN Z W,HU P,et al.Hierarchical consensus hashing for cross-modal retrieval［J］.IEEE Transactions on Multimedia,2023,26：824-836.

［9］KOBAYASHI S.Contextual augmentation：data augmentation by words with paradigmatic relations［J］.2018.

［10］WEI J,ZOU K.EDA：easy data augmentation techniques for boosting performance on text classification tasks［C］//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP),2019：6382-6388.

［11］ FADAEE M,BISAZZA A,MONZ C.Data augmentation for low-resource neural machine translation［C］// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2：Short Papers),2017：567-573.

［12］ZHAO W X,LIU J,REN R Y,et al.Dense text retrieval based on pretrained language models：a survey［J］.ACM Transactions on Information Systems,2024,42(4)：1-60.

［13］RAO J,DING L,QI S H,et al.Dynamic contrastive distillation for image-text retrieval［J］.IEEE Transactions on Multimedia,2023,25：8383-8395.

［14］LING C,ZHAO X J,LU J Y,et al.Domain specialization as the key to make large language models disruptive：a comprehensive survey［EB/OL］.(2024-03-29)［2024-07-22］.https：//arxiv.org/abs/2305.18703v7.

［15］LIU H R,MA Y,YAN M,et al.DiDA：disambiguated domain alignment for cross-domain retrieval with partial labels［C］//AAAI Conference on Artificial Intelligence,2024.

［16］郑敏.基于判别性特征学习的细粒度图像—文本检索研究［D］.北京：北京交通大学,2022.

［17］汪浩然.基于语义和常识指导的跨模态图文检索技术研究［D］.天津：天津大学,2021.

［18］DEVLIN J,CHANG M W,LEE K,et al.BERT：pre-training of deep bidirectional transformers for language understanding［EB/OL］.2018：1810.04805.https：//arxiv.org/abs/1810.04805v2.

［19］WANG X Y,DU Y J,CHEN D,et al.Constructing better prototype generators with 3D CNNs for few-shot text classification［J］.Expert Systems with Applications,2023,225：120124.

［20］KIM Y.Convolutional neural networks for sentence classification［C］//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP).Doha,Qatar.Stroudsburg,PA,USAACL,2014：1746-1751.

［21］LIU P,QIU X,XUANJING H.Recurrent Neural Network for Text Classification with Multi-Task Learning［C］//Proceeding of the 25th International Joint Conference on Artificial Intelligence.2016：2873-2879.

［22］QIN Y,PENG D Z,PENG X,et al.Deep evidential learning with noisy correspondence for cross-modal retrieval［C］//Proceedings of the 30th ACM International Conference on Multimedia.October 10-14,2022,Lisboa,Portugal.ACM,2022：4948-4956.

［23］GROENENDIJK R,KARAOGLU S,GEVERS T,et al.Multi-loss weighting with coefficient of variations［C］//2021 IEEE Winter Conference on Applications of Computer Vision (WACV).January 3-8,2021.Waikoloa,HI,USA.IEEE,2021：1469-1478.

［24］GAO T Y,YAO X C,CHEN D Q.SimCSE：simple contrastive learning of sentence embeddings［C］//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.Online and Punta Cana,Dominican Republic.Stroudsburg,PA,USAACL,2021：6894-6910.

［25］MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space［J］.1st International Conference on Learning Representations,ICLR 2013-Workshop Track Proceedings,2013：1-12.