Computer Science ›› 2025, Vol. 52 ›› Issue (3): 112-126.doi: 10.11896/jsjkx.240900095
• Database & Big Data & Data Science • Previous Articles Next Articles
WANG Yuan1, HUO Peng1, HAN Yi2, CHEN Tun2, WANG Xiang2, WEN Hui1
CLC Number:
| [1]MURPHY A H.What is a good forecast? an essay on the nature of goodness in weather forecasting[J].Weather and Forecasting,1993,8(2):281-293. [2]ZHANG Q J,HONG G,WU D L,et al.Development Status,Problems and Prospects of Agrometeorological Observation Operation in China[J].Chinese Journal of Agrometeorology,2023,44(8):735-749. [3]XIANG Y,CONG D M,ZHANG Y,et al.Two-Stream Neural Network Fusion Model for Highway Fog Detection[J].Journal of Southwest Jiaotong University,2019,54(1):173-179. [4]QIU S M,WANG X K,DU X L.Evaluation of Military Communication Effectiveness in Meteorological Environment Based on Optimized BP Neural Network[J].Fire Control & Command Control,2022(3):89-96. [5]KENDON E J,ROBERTS N M,FOWLER H J,et al.Heavier summer downpours with climate change revealed by weather forecast resolution model[J].Nature Climate Change,2014,4(7):570-576. [6]LORENC A C.Analysis methods for numerical weather prediction[J].Quarterly Journal of the Royal Meteorological Society,1986,112(474):1177-1194. [7]TAO S Y,ZHAO S X,ZHOU X P,et al.2003:The Research Progress of the Synoptic Meteorology and Synoptic Forecast[J].Chinese Journal of Atmospheric Sciences,2003,27(4):451-467. [8]SHEN X S,SU Y,HU J L,et al.Development and operationtransformation of GRAPES global middle-range forecast system[J].Appl Meteor Sci,2017,28(1):1-10. [9]BAUER P,THORPE A,BRUNET G.The quiet revolution ofnumerical weather prediction[J].Nature,2015,525(7567):47-55. [10]RICHARDSON L F.Weather prediction by numerical process[M].Combridge:Combridge University Press,1922. [11]MU M,CHEN B Y,ZHOU F F,et al.Methods and Uncertainties of Meteorological Forecast[J].Meteor Mon,2011,37(1):1-13. [12]SHEN X S,WANG J J,LI Z C,et al.China’s independent and innovative development of numerical weather prediction[J].Acta Meteorologica Sinica,2020,78(3):451-476. [13]BUIZZA R,MILLEER M,PALMER T N.Stochastic representation of model uncertainties in the ecmwf ensemble prediction system[J].Quarterly Journal of the Royal Meteorological Society,1999,125(560):2887-2908. [14]GNEITING T,RAFTERY A E.Weather forecasting with en-semble methods[J].Science,2005,310(5746):248-249. [15]SCHER S,MESSORI G.Predicting weather forecast uncertaintywith machine learning[J].Quarterly Journal of the Royal Me-teorological Society,2018,144(717):2830-2841. [16]SONG J Q,WU X J,ZHANG L L,et al.A Study of the Two Helmholtz Solvers in the GRAPES Model Using GCR and GMRES[J].Computer Engineering & Science,2011,33(11):65-70. [17]RÉMY S,KIPLING Z,FLEMMING J,et al.Description andevaluation of the tropospheric aerosol scheme in the european centre for medium-range weather forecasts(ecmwf) integrated forecasting system(ifs-aer,cycle 45r1)[J].Geoscientific Model Development,2019,12(11):4627-4659. [18]YANG F,PAN H L,KRUEGER S K,et al.Evaluation of the ncep global forecast system at the arm sgp site[J].Monthly Weather Review,2006,134(12):3668-3690. [19]SMITH D M,CUSACK S,COLMAN A W,et al.Improved surface temperature prediction for the coming decade from a global climate model[J].Science,2007,317(5839):796-799. [20]GROVER A,KAPOOR A,HORVITZ E.A deep hybrid model for weather forecasting[C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2015:379-386. [21]LYNCH P.The origins of computer weather prediction and climate modeling[J].Journal of Computational Physics,2008,227(7):3431-3444. [22]LECUN Y,BENGIO Y,HINTON G.Deep learning[J].Nature,2015,521(7553):436-444. [23]BERDEJO-ESPINOLA V,AMANO T.AI tools can improveequity inscience[J].Science,2023,379(6636):991-991. [24]HUANG X M,LIN Y L,XIONG W,et al.Research on international developments of AI large meteorological models in nume-rical forecasting[J].Trans Atmos Sci,2024,47(1):46-54. [25]ALABDULMOHSIN I M,NEYSHABUR B,ZHAI X.Revisiting neural scaling laws in language and vision[J].Advances in Neural Information Processing Systems,2022,35:22300-22312. [26]SHI X,CHEN Z,WANG H,et al.Convolutional lstm network:A machine learning approach for precipitation nowcasting[C]//Proceedings of the 28th International Conference on NEURAL Information Processing Systems.2015:802-810. [27]MARKOVICS D,MAYER M J.Comparison of machine learningmethods for photovoltaic power forecasting based on numerical weather prediction[J].Renewable and Sustainable Energy Reviews,2022,161:112364. [28]REN X,LI X,REN K,et al.Deep learning-based weather prediction:A survey[J].Big Data Research,2021,23:100178. [29]JASEENA K U,KOVOOR B C.Deterministic weather forecasting models based on intelligent predictors:A survey[J].Journal of King Saud University-computer and Information Sciences,2022,34(6):3393-3412. [30]ABDALLA A M,GHAITH I H,TAMIMI A A.Deep learning weather forecasting techniques:literature survey[C]//2021 International Conference on Information Technology(ICIT).IEEE,2021:622-626. [31]YANG X,DAI K,ZHU Y J.Progress and challenges of deeplearning techniques in intelligent grid weather forecasting[J].Acta Meteorologica Sinica,2022,80(5):649-667. [32]LAZO J K,MORSS R E,DEMUTH J L.300 billion served:Sources,perceptions,uses,and values of weather forecasts[J].Bulletin of the American Meteorological Society,2009,90(6):785-798. [33]SUN J,CAO Z,LI H,et al.Application of artificial intelligence technology to numerical weather prediction[J].Journal of Application Meteorological Science,2021,32(1):1-11. [34]ESTÉVEZ J,GAVILÁN P,GIRÁLDEZ J V.Guidelines on validation procedures for meteorological data from automatic wea-ther stations[J].Journal of Hydrology,2011,402(1/2):144-154. [35]CARRASSI A,BOCQUET M,BERTINO L,et al.Data assimilation in the geosciences:An overview of methods,issues,and perspectives[J].Wiley Interdisciplinary Reviews:Climate Change,2018,9(5):e535. [36]WANG X,MA Y,WANG Y,et al.Traffic flow prediction via spatial temporal graph neural network[C]//Proceedings of the Web Conference 2020.2020:1082-1092. [37]GAO F,YANG Y,WANG J,et al.A deep convolutional generative adversarial networks(DCGANs)-based semi-supervised method for object recognition in synthetic aperture radar(SAR) images[J].Remote Sensing,2018,10(6):846. [38]WANG B,ZOU X,ZHU J.Data assimilation and its applications[J].Proceedings of the National Academy of Sciences,2000,97(21):11143-11144. [39]KHODARAHMI M,MAIHAMI V.A review on kalman filter models[J].Archives of Computational Methods in Engineering,2023,30(1):727-747. [40]COURTIER P,ANDERSSON E,HECKLEY W,et al.The ECMWF implementation of three-dimensional variational assimilation(3D-var).I:Formulation[J].Quarterly Journal of the Royal Meteorological Society,1998,124(550):1783-1807. [41]AMARI S I.Backpropagation and stochastic gradient descentmethod[J].Neurocomputing,1993,5(4/5):185-196. [42]FLETCHER R,REEVES C M.Function minimization by conju-gate gradients[J].The Computer Journal,1964,7(2):149-154. [43]HERSBACH H,BELL B,BERRISFORD P,et al.The era5global reanalysis[J].Quarterly Journal of the Royal Meteorological Society,2020,146(730):1999-2049. [44]RASP S,DUEBEN P D,SCHER S,et al.Weatherbench:abenchmark data set for data-driven weather forecasting[J].Journal of Advances in Modeling Earth Systems,2020,12(11):e2020MS002203. [45]RASP S,HOYER S,MEROSE A,et al.Weatherbench 2:Abenchmark for the next generation of data-driven global wea-ther models[J].Journal of Advances in Modeling Earth Systems,2024,16(6):e2023MS004019. [46]JUMPER J,EVANS R,PRITZEL A,et al.Highly accurate protein structure prediction with alphafold[J].Nature,2021,596(7873):583-589. [47]SINGHAL K,AZIZI S,TU T,et al.Large language models encode clinical knowledge[J].Nature,2023,620(7972):172-180. [48]ABRAMSON J,ADLER J,DUNGER J,et al.Accurate struc-ture prediction of biomolecular interactions with alphafold 3[J].Nature,2024,630:493-500. [49]MERCHANT A,BATZNER S,SCHOENHOLZ S S,et al.Sca-ling deep learning for materials discovery[J].Nature,2023,624(7990):80-85. [50]KHARE D,CHEN Z,GULOTTY R,et al.Probabilistic physics-integrated neural differentiable modeling for isothermal chemical vapor infiltration process[J].NPJ Computational Materials,2024,10(1):120. [51]REICHSTEIN M,CAMPS-VALLS G,STEVENS B,et al.Deep learning and process understanding for data-driven earth system science[J].Nature,2019,566(7743):195-204. [52]BENGIO Y.Learning deep architectures for AI[J].Foundations and Trends in Machine Learning,2009,2(1):1-127. [53]LI M,ZHANG T,CHEN Y,et al.Efficient mini-batch training for stochastic optimization[C]//Proceedings of the 20th ACM SIGKDD International Conference on KNOWLEDGE Discovery and Data Mining.2014:661-670. [54]LI Z,LIU F,YANG W,et al.A survey of convolutional neural networks:analysis,applications,and prospects[J].IEEE Transactions on Neural Networks and Learning Systems,2021,33(12):6999-7019. [55]SCHUSTER M,PALIWAL K K.Bidirectional recurrent neural networks[J].IEEE Transactions on Signal Processing,1997,45(11):2673-2681. [56]WU Z,PAN S,CHEN F,et al.A comprehensive survey ongraph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2020,32(1):4-24. [57]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is all you need[J].Advances in Neural Information Processing Systems,2017,11(4):6000-6010. [58]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[J].Communications of the ACM,2017,60(6):84-90. [59]HAN K,WANG Y,CHEN H,et al.A survey on vision transformer[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2022,45(1):87-110. [60]ZHU Y,TOTH Z,WOBUS R,et al.The economic value of ensemble-based weather forecasts[J].Bulletin of the American Me-teorological Society,2002,83(1):73-84. [61]GLAHN H R,RUTH D P.The new digital forecast database of the national weather service[J].Bulletin of the American Me-teorological Society,2003,84(2):195-202. [62]RODRIGUES E R,OLIVEIRA I,CUNHA R,et al.Deepdownscale:A deep learning strategy for high-resolution weather forecast[J].arXiv:1808.05264,2018. [63]BAI C,SUN F,ZHANG J,et al.Rainformer:Features extraction balanced network for radar-based precipitation nowcasting[J].IEEE Geoscience and Remote Sensing Letters,2022,19:1-5. [64]RAVURI S,LENC K,WILLSON M,et al.Skilful precipitation nowcasting using deep generative models of radar[J].Nature,2021,597(7878):672-677. [65]ANDRYCHOWICZ M,ESPEHOLT L,LI D,et al.Deep lear-ning for day forecasts from sparse observations[J].arXiv:2306.06079,2023. [66]GAO Z,SHI X,WANG H,et al.Earthformer:Exploring space-time transformers for earth system forecasting[J].Advances in Neural Information Processing Systems,2022,35:25390-25403. [67]MOON S H,KIM Y H,LEE Y H,et al.Application of machine learning to an early warning system for very short-term heavy rainfall[J].Journal of Hydrology,2019,568:1042-1054. [68]LEBEDEV V,IVASHKIN V,RUDENKO I,et al.Precipitation nowcasting with satellite imagery[C]//Proceedings of the 25th ACM SIGKDD International Conference on KnowledgeDisco-very & Data Mining.2019:2680-2688. [69]RONNEBERGER O,FISCHER P,BROX T.U-net:Convolu-tional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-assisted Intervention-MICCAI 2015:18th International Conference,Munich,Germany,October 5-9,2015,Proceedings,Part III 18.Springer,2015:234-241. [70]SUN D,ROTH S,BLACK M J.A quantitative analysis of current practices in optical flow estimation and the principles behind them[J].International Journal of Computer Vision,2014,106:115-137. [71]AGRAWAL S,BARRINGTON L,BROMBERG C,et al.Ma-chine learning for precipitation nowcasting from radar images[J].arXiv:1912.12132,2019. [72]ZHANG J,HOWARD K,LANGSTON C,et al.Multi-radarmulti-sensor(mrms) quantitative precipitation estimation:Initial operating capabilities[J].Bulletin of the American Meteorological Society,2016,97(4):621-638. [73]WU H,YAO Z,WANG J,et al.Motionrnn:A flexible model for video prediction with spacetime-varying motions[C]//Procee-dings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:5435-15444. [74]ZHOU C,SUN C,LIU Z,et al.A c-lstm neural network for text classification[J].arXiv:1511.08630,2015. [75]WANG Y,WU H,ZHANG J,et al.Predrnn:A recurrent neural network for spatiotemporal predictive learning[J].IEEE Tran-sactions on Pattern Analysis and Machine Intelligence,2022,45(2):2208-2225. [76]WU X H,HUA Y J,GUAN Y H,et al.Application of CNN-At-tention-BP to precipitation forecast[J].Journal of Nanjing University of Information Science & Technology,2022,14(2):148-155. [77]PAN B,HSU K,AGHAKOUCHAK A,et al.Improving preci-pitation estimation using convolutional neural network[J].Water Resources Research,2019,55(3):2301-2321. [78]WERBOS P J.Backpropagation through time:what it does and how to do it[J].Proceedings of the IEEE,1990,78(10):1550-1560. [79]SHIN S I,NEWMAN M.Seasonal predictability of global and north american coastal sea surface temperature and height anomalies[J].Geophysical Research Letters,2021,48(10):e2020GL091886. [80]SANDERY P A,SAKOV P.Ocean forecasting of mesoscale features can deteriorate by increasing model resolution towards the submesoscale[J].Nature Communications,2017,8(1):1566. [81]ZHANG Q,WANG H,DONG J,et al.Prediction of sea surface temperature using long short-term memory[J].IEEE Geoscience and Remote Sensing Letters,2017,14(10):1745-1749. [82]YANG Y,DONG J,SUN X,et al.A cfcc-lstm model for sea surface temperature prediction[J].IEEE Geoscience and Remote Sensing Letters,2017,15(2):207-211. [83]LIN X G,WANG Z Y,LI J S,et al.Sea temperature forecasting based on LSTM neural network along the coast of eastern Guangdong[J].Marine Forecasts,2022,39(5):27-36. [84]WENG S J,CAI J H,PANG Y X,et al.Application of convolutional neural network to sea surface temperature prediction in the coastal waters[J].Journal of Tropical Oceanography,2024,43(1):40-47. [85]CHEN W B,CHEN H,HSIAO S C,et al.Wind forcing effect on hind-casting of typhoon-driven extreme waves[J].Ocean Engineering,2019,188:106260. [86]GONG Y,DONG S,WANG Z.Forecasting of typhoon wavebased on hybrid machine learning models[J].Ocean Enginee-ring,2022,266:112934. [87]CHEN S T.Probabilistic forecasting of coastal wave height du-ring typhoon warning period using machine learning methods[J].Journal of Hydroinformatics,2019,21(2):343-358. [88]ZHOU S H,HONG X,LIANG C X,et al.A method of tropical cyclone wave height calculation based on artificial meural network[J].Journal of Tropical Oceanography,2020,39(4):25-33. [89]SHANG F C,LI C Q,ZHAN K,et al.Application of Improved LSTM Neural Network in Time-Series Prediction of Extreme Short-Term Wave[J].Journal of Shanghai Jiao Tong University,2023,57(6):659-665. [90]REICHENBACH P,ROSSI M,MALAMUD B D,et al.A review of statistically-based landslide susceptibility models[J].Earth-science Reviews,2018,180:60-91. [91]TIAN N M,LAN H X,WU Y M,et al.Performance Comparison of BP Artificial Neural Network and CART Decision Tree Model in Landslide Susceptibility Prediction[J].Journal of Geo-information Science,2020,22(12):2304-2316. [92]YANG J K,DANG J W,YANG J Y,et al.Displacement prediction of step-like landslide based on temporal analysis and CNN-BiLSTM-AM[J].Foreign Electronic Measurement Technology,2024,43(1):126-134. [93]HAO Z,HAO F,SINGH V P.A general framework for multivariate multi-index drought prediction based on multivariate ensemble stream-flow prediction(mesp)[J].Journal of Hydrology,2016,539:1-10. [94]HAO Z,AGHAKOUCHAK A.A nonparametric multivariatemulti-index drought monitoring framework[J].Journal of Hydrometeorology,2014,15(1):89-101. [95]NGUYEN T,BRANDSTETTER J,KAPOOR A,et al.Climax:A foundation model for weather and climate[J].arXiv:2301.10343,2023. [96]SCHULTZ M G,BETANCOURT C,GONG B,et al.Can deep learning beat numerical weather prediction?[J].Philosophical Transactions of the Royal Society A,2021,379(2194):20200097. [97]KURTH T,SUBRAMANIAN S,HARRINGTON P,et al.Fourcastnet:Accelerating global high-resolution weather forecasting using adaptive fourier neural operators[C]//Procee-dings of the Platform for Advanced Scientific Computing Confe-rence.2023:1-11. [98]GUIBAS J,MARDANI M,LI Z,et al.Adaptive fourier neural operators:Efficient token mixers for transformers[J].arXiv:2111.13587,2021. [99]BI K,XIE L,ZHANG H,et al.Pangu-weather:A 3D high-resolution model for fast and accurate global weather forecast[J].arXiv:2211.02556,2022. [100]BI K,XIE L,ZHANG H,et al.Accurate medium-range global weather forecasting with 3D neural networks[J].Nature,2023,619(7970):533-538. [101]LIU Z,LIN Y,CAO Y,et al.Swin transformer:Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:10012-10022. [102]LAM R,SANCHEZ-GONZALEZ A,WILLSON M,et al.Graphcast:Learning skillful medium-range global weather forecasting[J].arXiv:2212.12794,2022. [103]LAM R,SANCHEZ-GONZALEZ A,WILLSON M,et al.Learning skillful medium-range global weather forecasting[J].Science,2023,382(6677):1416-1421. [104]CHEN K,HAN T,GONG J,et al.Fengwu:Pushing the skillful global medium-range weather forecast beyond 10 days lead[J].arXiv:2304.02948,2023. [105]RADFORD A,KIM J W,HALLACY C,et al.Learning transferable visual models from natural language supervision[C]//International Conference on Machine Learning.PMLR,2021:8748-8763. [106]CHERTI M,BEAUMONT R,WIGHTMAN R,et al.Reprodu-cible scaling laws for contrastive language-image learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2023:2818-2829. [107]CHEN L,ZHONG X,ZHANG F,et al.Fuxi:A cascade machine learning forecasting system for 15-day global weather forecast[J].Npj Climate and Atmospheric Science,2023,6(1):190. [108]LIU Z,HU H,LIN Y,et al.Swin transformer v2:Scaling up capacity and resolution[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:12009-12019. [109]MAGNUSSON L,BIDLOT J R,BONAVITA M,et al.Ecmwf activities for improved hurricane forecasts[J].Bulletin of the American Meteorological Society,2019,100(3):445-458. [110]CHEN K,BAI L,LING F,et al.Towards an end-to-end artificial intelligence driven global weather forecasting system[J].arXiv:2312.12462,2023. [111]YU J,LIN Z,YANG J,et al.Free-form image inpainting with gated convolution[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:4471-4480. [112]XIAO Y,BAI L,XUE W,et al.Fengwu-4Dvar:Coupling the data-driven weather forecasting model with 4D variational assimilation[J].arXiv:2312.12455,2023. [113]FABLET R,CHAPRON B,DRUMETZ L,et al.Learning variational data assimilation models and solvers[J].Journal of Advances in Modeling Earth Systems,2021,13(10):e2021MS002572. [114]XU X,SUN X,HAN W,et al.Fuxi-da:A generalized deeplearning data assimilation framework for assimilating satellite observations[J].arXiv:2404.08522,2024. [115]KASHINATH K,MUSTAFA M,ALBERT A,et al.Physics-informed machinelearning:case studies for weather and climate modelling[J].Philosophical Transactions of the Royal Society A,2021,379(2194):20200093. [116]ARCOMANO T,SZUNYOGH I,WIKNER A,et al.A hybrid approach to atmospheric modeling that combines machine lear-ning with a physics－based numerical model[J].Journal of Advances in Modeling Earth Systems,2022,14(3):e2021MS002712. |
| [1] | WANG Tao, BAI Xuefei, WANG Wenjian. Selective Feature Fusion for 3D CT Image Segmentation of Renal Cancer Based on Edge Enhancement [J]. Computer Science, 2025, 52(3): 41-49. |
| [2] | WANG Jie, WANG Chuangye, XIE Jiucheng, GAO Hao. Animatable Head Avatar Reconstruction Algorithm Based on Region Encoding [J]. Computer Science, 2025, 52(3): 50-57. |
| [3] | ZHONG Yue, GU Jieming. 3D Reconstruction of Single-view Sketches Based on Attention Mechanism and Contrastive Loss [J]. Computer Science, 2025, 52(3): 77-85. |
| [4] | SHEN Yaxin, GAO Lijian , MAO Qirong. Semi-supervised Sound Event Detection Based on Meta Learning [J]. Computer Science, 2025, 52(3): 222-230. |
| [5] | HAN Lin, WANG Yifan, LI Jianan, GAO Wei. Automatic Scheduling Search Optimization Method Based on TVM [J]. Computer Science, 2025, 52(3): 268-276. |
| [6] | SUN Rui, WANG Fei, FENG Huidong, ZHANG Xudong, GAO Jun. Research Progress in Facial Presentation Attack Detection Methods Based on Deep Learning [J]. Computer Science, 2025, 52(2): 323-335. |
| [7] | DING Ruiyang, SUN Lei, DAI Leyu, ZANG Weifei, XU Bayi. Generation Method for Adversarial Networks Traffic Based on Universal Perturbations [J]. Computer Science, 2025, 52(2): 336-343. |
| [8] | CHEN Zigang, PAN Ding, LENG Tao, ZHU Haihua, CHEN Long, ZHOU Yousheng. Explanation Robustness Adversarial Training Method Based on Local Gradient Smoothing [J]. Computer Science, 2025, 52(2): 374-379. |
| [9] | ZHANG Yusong, XU Shuai, YAN Xingyu, GUAN Donghai, XU Jianqiu. Survey on Cross-city Human Mobility Prediction [J]. Computer Science, 2025, 52(1): 102-119. |
| [10] | LIU Yuming, DAI Yu, CHEN Gongping. Review of Federated Learning in Medical Image Processing [J]. Computer Science, 2025, 52(1): 183-193. |
| [11] | LI Yujie, MA Zihang, WANG Yifu, WANG Xinghe, TAN Benying. Survey of Vision Transformers(ViT) [J]. Computer Science, 2025, 52(1): 194-209. |
| [12] | ZHU Xiaoyan, WANG Wenge, WANG Jiayin, ZHANG Xuanping. Just-In-Time Software Defect Prediction Approach Based on Fine-grained Code Representationand Feature Fusion [J]. Computer Science, 2025, 52(1): 242-249. |
| [13] | ZHANG Jian, LI Hui, ZHANG Shengming, WU Jie, PENG Ying. Review of Pre-training Methods for Visually-rich Document Understanding [J]. Computer Science, 2025, 52(1): 259-276. |
| [14] | LI Yahe, XIE Zhipeng. Active Learning Based on Maximum Influence Set [J]. Computer Science, 2025, 52(1): 289-297. |
| [15] | ZHANG Xin, ZHANG Han, NIU Manyu, JI Lixia. Adversarial Sample Detection in Computer Vision:A Survey [J]. Computer Science, 2025, 52(1): 345-361. |
|
||
Home