Multimodal Sentiment Detection Based on Multi-channel Graph Neural Networks

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545

Semi-supervised classification with graph convolutional networks

Kipf, T. N., & Welling, M. (2016) · arXiv · doi:10.48550/arxiv.1609.02907 · Cited by 24,853

YOLOv3: An Incremental Improvement

Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141

Convolutional Neural Networks for Sentence Classification

Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1181 · Cited by 14,831

A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)

Khanam, Z., Achari, V. P. S., Boukhennoufa, I., et al. (2024) · Durham Research Online (Durham University) · doi:10.4230/oasics.ng-res.2024.2 · Cited by 14,048

Neural Message Passing for Quantum ChemistryPreprint

Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,678

Places: A 10 Million Image Database for Scene Recognition

Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., & Torralba, A. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2723009 · Cited by 4,042

Recurrent Convolutional Neural Networks for Text Classification

Lai, S., Xu, L., Liu, K., & Zhao, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9513 · Cited by 2,384

Graph Convolutional Networks for Text Classification

Yao, L., Mao, C., & Luo, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017370 · Cited by 2,224

Deep learning for sentiment analysis: A survey

Zhang, L., Wang, S., & Liu, B. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1253 · Cited by 2,182

Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification

Zhou, P., Shi, W. Y., Tian, J., Qi, Z., Li, B., Hao, H., & Xu, B. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-2034 · Cited by 2,147

GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering

Hudson, D. A., & Manning, C. D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00686 · Cited by 1,797

Multi-Label Image Recognition With Graph Convolutional Networks

Chen, Z.-M., Wei, X.-S., Wang, P., & Guo, Y. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00532 · Cited by 1,364

Hierarchical Question-Image Co-Attention for Visual Question AnsweringPreprint

Lu, J., Yang, J., Batra, D., & Parikh, D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.00061 · Cited by 1,357

MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis

Hazarika, D., Zimmermann, R., & Poria, S. (2020) · Proceedings of the 28th ACM International Conference on Multimedia · doi:10.1145/3394171.3413678 · Cited by 1,073

A survey of multimodal sentiment analysis

Soleymani, M., García, D., Jou, B., Schuller, B. W., Chang, S., & Pantić, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.08.003 · Cited by 647

A survey of sentiment analysis in social media

Yue, L., Chen, W., Li, X., Zuo, W., & Yin, M. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1236-4 · Cited by 594

Hierarchical Question-Image Co-Attention for Visual Question Answering

Lu, J. (2024) · TIB Data Manager · doi:10.57702/uhdmfu1r · Cited by 357

Text Level Graph Neural Network for Text Classification

Huang, L., Ma, D., Li, S., Zhang, X., & Wang, H. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1345 · Cited by 348

Tensor Graph Convolutional Networks for Text Classification

Liu, X., You, X., Zhang, X., Wu, J. Y., & Lv, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6359 · Cited by 308

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

George, A., Mostaani, Z., Geissenbuhler, D., Nikisins, O., Anjos, A., & Marcel, S. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2019.2916652 · Cited by 308

Image-Text Multimodal Emotion Classification via Multi-View Attentional Network

Yang, X., Feng, S., Wang, D., & Zhang, Y. (2020) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2020.3035277 · Cited by 273

Sentiment Analysis on Multi-View Social Data

Niu, T., Zhu, S., Pang, L., & Saddik, A. E. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-27674-8_2 · Cited by 272

Untitled work

(2013) · Cited by 236

A Co-Memory Network for Multimodal Sentiment Analysis

Xu, N., Mao, W., & Chen, G. (2018) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3209978.3210093 · Cited by 204

Cross-modality Consistent Regression for Joint Visual-Textual Sentiment Analysis of Social Multimedia

You, Q., Luo, J., Jin, H., & Yang, J. (2016) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2835776.2835779 · Cited by 199

Fake News Detection via Knowledge-driven Multimodal Graph Convolutional Networks

Wang, Y., Qian, S., Hu, J., Fang, Q., & Xu, C. (2020) · International Conference on Multimedia Retrieval · doi:10.1145/3372278.3390713 · Cited by 190

MultiSentiNet

Xu, N., & Mao, W. (2017) · doi:10.1145/3132847.3133142 · Cited by 185

TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis

Wang, Z., Wan, Z., & Wan, X. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380000 · Cited by 156

Learning One Class Representations for Face Presentation Attack Detection Using Multi-Channel Convolutional Neural Networks

George, A., & Marcel, S. (2020) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2020.3013214 · Cited by 126

Analyzing multimodal public sentiment based on hierarchical semantic attentional network

Xu, N. (2017) · IEEE International Conference on Intelligence and Security Informatics (ISI) · doi:10.1109/isi.2017.8004895 · Cited by 109

Multimodal Sentiment Analysis

Kaur, R., & Kautish, S. (2019) · International Journal of Service Science Management Engineering and Technology · doi:10.4018/ijssmet.2019040103 · Cited by 73

Iterative Context-Aware Graph Inference for Visual Dialog

Guo, D., Wang, H., Zhang, H., Zha, Z.-J., & Wang, M. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01007 · Cited by 57

KBGN: Knowledge-Bridge Graph Network for Adaptive Vision-Text Reasoning in Visual Dialogue

Jiang, X., Du, S., Qin, Z., Sun, Y., & Yu, J. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413826 · Cited by 40

Multimodal Neural Graph Memory Networks for Visual Question Answering

Khademi, M. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.643 · Cited by 33

Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition

Islam, J., Mercer, R. E., & Xiao, L. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1137 · Cited by 29

Multi-Modal Sentiment Classification With Independent and Interactive Knowledge via Semi-Supervised Learning

Zhang, D., Li, S., Zhu, Q., & Zhou, G. (2020) · IEEE Access · doi:10.1109/access.2020.2969205 · Cited by 28

Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories

Chen, T., Wang, Z., Li, L., Li, D., Li, Z., Chang, X., Bian, P., Liang, G., Wang, Q., & Xie, T. (2025) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.ecoop.2025.6

Multimodal learning with transformers: A survey

Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072

Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network

Liang, B., Lou, C., Li, X., Yang, M., Gui, L., He, Y., Pei, W., & Xu, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.124 · Cited by 178

Vision-Language Pre-Training for Multimodal Aspect-Based Sentiment Analysis

Yan, L., Yu, J., & Xia, R. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.152 · Cited by 169

CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection

Li, Z., Xu, B. H., Zhu, C., & Zhao, T. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.175 · Cited by 157

Targeted Multimodal Sentiment Classification based on Coarse-to-Fine Grained Image-Target Matching

Yu, J., Wang, J., Xia, R., & Li, J. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/622 · Cited by 67

MMGCN: Multimodal Fusion via Deep Graph Convolution Network for Emotion Recognition in Conversation

Hu, J., Liu, Y., Zhao, J., & Jin, Q. (2021) · doi:10.18653/v1/2021.acl-long.440 · Cited by 368

A multi-relational graph convolutional network for multimodal sentiment analysis

Dau, H. M., & Phan, H. T. (2026) · International Journal of Intelligent Information and Database Systems · doi:10.1504/ijiids.2026.10078616

Graph Neural Network-Based Multimodal Emotion Recognition

Al-Saadawi, H. F. T. (2024) · doi:10.13140/rg.2.2.33721.74081

Multimodal Multi-loss Fusion Network for Sentiment Analysis

Wu, Z., Gong, Z., Koo, J., & Hirschberg, J. (2024) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2024.naacl-long.197 · Cited by 90

Multimodal sentiment analysis in social media using deep learning with convolutional neural networks

Majumder, N. (2018) · Cited by 2