Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211000161-7.doi: 10.11896/jsjkx.211000161
• Image Processing & Multimedia Technology • Previous Articles Next Articles
WU He-xiang, WANG Zhong-qing, LI Pei-feng
CLC Number:
| [1]ALAM F,OFLI F,IMRAN M.CrisisMMD:Multimodal twitter datasets from natural disasters[C]//Proceedings of the Twelfth International Conference on Web and Social Media.California,USA:2018:465-473. [2]OFLI F,ALAM F,IMRAN M.Analysis of social media datausing multimodal deep learning for disaster response[C]//Proceedings of the 17th ISCRAM Conference.Blacksburg,VA,USA:2020. [3]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association of Computational Linguistics:Human Language Technologies.Minneapolis,Minnesota:Association for Computational Linguistics,2019:4181-4186. [4]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//Proceedings of the 3rd International Conference on Learning Representations.San Diego,CA,USA:2015. [5]YANG Z,HE X,GAO J,et al.Stacked attention networks forimage question answering[C]//Proceedings of the IEEE Con-ference on Computer Vision and Pattern Recognition.Las Vegas,NV,USA,2016:21-29. [6]LIAO S,GRISHMAN R.Using document level cross-event inference to improve event extraction[C]//Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics.Uppsala,Sweden,2010:789-797. [7]HONG Y,ZHANG J,MA B,et al.Using cross-entity inference to improve event extraction[C]//Proceedings of the 49th AnnualMeeting of the Association for Computational Linguistics.Portland,Oregon,USA,2011:1127-1136. [8]LI Q,JI H,HUANG L.Joint event extraction via structured prediction with global features[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics.Sofia,Bulgaria,2013:73-82. [9]CHEN Y,XU L,LIU K,et al.Event extraction via dynamic multi-pooling convolutional neural networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computa-tional Linguistics and the 7th International Joint Conference on Natural Language Processing.2015:167-176. [10]NGUYEN T H,CHO K,GRISHMAN R.Joint event extraction via recurrent neural networks[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics.Berlin,Germany,2016:300-309. [11]YANG S,FENG D,QIAO L,et al.Exploring pretrained lan-guage models for event extraction and generation[C]//Procee-dings of the 57th Annual Meeting of the Association for Computational Linguistics.Florence,Italy,2019:5284-5294. [12]SHA L,QIAN F,CHANG B,et al.Jointly extracting event triggers and arguments by dependency-bridge RNN and tensor-based argument interaction[C]//Proceedings of the Thirty-Se-cond AAAI Conference on Artificial Intelligence.New Orleans,Louisiana,USA,2018:5916-5923. [13]HOCHREITER S,SCHMIDHUBER J.Long short-term Memory[J].Neural Computation,1997,9(8):1735-1780. [14]LIU X,LUO Z,HUANG H.Jointly multiple events extraction via attention-based graph information Aggregation[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.Brussels,Belgium,2018:1247-1256. [15]WANG X,HAN X,LIU Z,et al.Adversarial training for weakly supervised event detection[C]//Proceedings of the 2019Confe-rence of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.Min-neapolis,MN,USA,2019:998-1008. [16]TONG M,XU B,WANG S,et al.Improving event detection via open-domain trigger knowledge[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Online,ACL,2020:5887-5897. [17]ZHANG T,WHITEHEAD S,ZHANG H,et al.ImprovingEvent Extraction via Multimodal Integration[C]//Proceedings of the 2017 ACM on Multimedia Conference.Mountain View,CA,USA,2017:270-278. [18]LI M,ZAREIAN A,ZENG Q,et al.Cross-media structuredcommon space for multimedia event extraction[C]//Procee-dings of the 58th Annual Meeting of the Association for Computational Linguistics.Online,2020:2557-2568. [19]TONG M,WANG S,CAO Y,et al.Image enhanced event detection in news articles[C]//Proceedings of the 34th AAAI Confe-rence on Artificial Intelligence.New York,NY,USA,2020:9040-9047. [20]D’MELLO S K,KORY J M.A review and meta-analysis of multimodal affect detection systems[J].ACM Computing Surveys,2015,47(3):43:1-43:36. [21]MORVANT E,HABRARD A,AYACHE S.Majority vote of diverse classifiers for late fusion[C]//Structural,Syntactic,and Statistical Pattern Recognition-Joint IAPR International Workshop.Joensuu,Finland,2014:153-162. [22]WANG Y,HUANG W,SUN F,et al.Deep multimodal fusion by channel exchanging[C]//Proceedings of the Thirty-fourth Conference on Neural Information Processing Systems.Vir-tual,2020. [23]PEREZ-RUA J M,VIELZEUF V,PATEUX S,et al.MFAS:Multimodal fusion architecture search[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Long Beach,CA,USA,2019:6959-6968. [24]ZADEH A,CHEN M,PORIA S,et al.Tensor fusion network for multimodal sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Proces-sing.Copenhagen,Denmark,2017:1103-1114. [25]SAHU G,VECHTOMOVA O.Adaptive fusion techniques for multimodal data[C]//Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics:Main Volume.2021:3156-3166. [26]HORI C,HORI T,LEE T Y,et al.Attention-based multimodal fusion for video description[C]//Proceedings of the IEEE International Conference on Computer Vision.Venice,Italy,2017:4203-4212. [27]KIM Y.Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing.Doha,Qatar,2014:1746-1751. [28]LU J,YANG J.BATRA D,et al.Hierarchical question-imageco-attention for visual question answering[C]//Proceedings of the Thirtieth Annual Conference on Neural Information Processing Systems.Barcelona,Spain,2016:289-297. [29]NAM H,HA J W,KIM J.Dual attention networks for multimodal reasoning and matching[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition.Honolulu,HI,USA,2017:2156-2164. [30]MAJUMDAR A,SHRIVASTAVA A,LEE S,et al.Improving vision-and-language navigation with image-text pairs from the web[C]//Proceedings of the 2020 European Conference on Computer Vision.Glasgow,UK,ECCV,2020:259-274. [31]DENG J,DONG W,SOCHER R,et al.Imagenet:A large-scale hierarchical image database[C]//Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Miami,Florida,USA,2009:248-255. [32]ALI S R,HOSSEIN A,JOSEPHINE S,et al.CNN features off-the-shelf:An astounding baseline for recognition[C]//Procee-dings of the IEEE Conference on Computer Vision and Pattern Recognition,CVPR Workshops 2014.Columbus,OH,USA,2014:512-519. [33]KINGMA D P,BA J.Adam:A method for stochastic optimization[C]//Proceedings of the 3rd International Conference on Learning Representations,ICLR 2015.San Diego,Ca,USA. [34]KIELA D,BHOOSHAN S,FIROOZ H,et al.Supervised multimodal bitransformers for classifying images and text[J].arXiv:1909.02950. [35]LI X,YIN X,LI C,et al.Oscar:Object-semantic aligned pre-training for vision-language tasks[C]//Proceedings of the 16th European Conference on Computer Vision.Glasgow,UK,2020:121-137. |
| [1] | ZHOU Fang-quan, CHENG Wei-qing. Sequence Recommendation Based on Global Enhanced Graph Neural Network [J]. Computer Science, 2022, 49(9): 55-63. |
| [2] | DAI Yu, XU Lin-feng. Cross-image Text Reading Method Based on Text Line Matching [J]. Computer Science, 2022, 49(9): 139-145. |
| [3] | ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161. |
| [4] | XIONG Li-qin, CAO Lei, LAI Jun, CHEN Xi-liang. Overview of Multi-agent Deep Reinforcement Learning Based on Value Factorization [J]. Computer Science, 2022, 49(9): 172-182. |
| [5] | RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207. |
| [6] | WANG Ming, PENG Jian, HUANG Fei-hu. Multi-time Scale Spatial-Temporal Graph Neural Network for Traffic Flow Prediction [J]. Computer Science, 2022, 49(8): 40-48. |
| [7] | ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119. |
| [8] | SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177. |
| [9] | YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236. |
| [10] | JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335. |
| [11] | ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105. |
| [12] | ZENG Zhi-xian, CAO Jian-jun, WENG Nian-feng, JIANG Guo-quan, XU Bin. Fine-grained Semantic Association Video-Text Cross-modal Entity Resolution Based on Attention Mechanism [J]. Computer Science, 2022, 49(7): 106-112. |
| [13] | XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141. |
| [14] | MENG Yue-bo, MU Si-rong, LIU Guang-hui, XU Sheng-jun, HAN Jiu-qiang. Person Re-identification Method Based on GoogLeNet-GMP Based on Vector Attention Mechanism [J]. Computer Science, 2022, 49(7): 142-147. |
| [15] | JIN Fang-yan, WANG Xiu-li. Implicit Causality Extraction of Financial Events Integrating RACNN and BiLSTM [J]. Computer Science, 2022, 49(7): 179-186. |
|
||
Home