Most cited papers in Multimodal Machine Learning Applications
What are the most cited papers in Multimodal Machine Learning Applications?
The most cited paper in Multimodal Machine Learning Applications is "Exploiting Generative AI to Scale up Intelligent Tutoring Systems" (Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra et al., 2023), with 79,061 citations. Next come "ImageNet: A large-scale hierarchical image database" (63,518) and "Microsoft COCO: Common Objects in Context" (43,802).
Showing 50 of 97 papers. Every paper with more than 1,840 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Exploiting Generative AI to Scale up Intelligent Tutoring Systems Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) | 2023 | 79,061 |
| 2 | ImageNet: A large-scale hierarchical image database Jia Hao Deng, Wei Dong, Richard Socher et al. · 2009 IEEE Conference on Computer Vision and Pattern Recognition | Jun 2009 | 63,518 |
| 3 | Microsoft COCO: Common Objects in Context Tsung-Yi Lin, Michael Maire, Serge Belongie et al. · Lecture notes in computer science | 2014 | 43,802 |
| 4 | Fully convolutional networks for semantic segmentation Jonathan Long, Evan Shelhamer, Trevor J. Darrell | Jun 2015 | 37,703 |
| 5 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Kumar Das et al. | Oct 2017 | 23,064 |
| 6 | YOLO9000: Better, Faster, Stronger Joseph Redmon, Ali Farhadi | Jul 2017 | 19,320 |
| 7 | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick et al. · arXiv (Cornell University) | 4 Jun 2015 | 18,225 |
| 8 | Pyramid Scene Parsing Network Hengshuang Zhao, Jianping Shi, Xiaojuan Qi et al. | Jul 2017 | 15,943 |
| 9 | Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation Liang-Chieh Chen, Yukun Zhu, George Papandreou et al. · Lecture notes in computer science | 2018 | 15,130 |
| 10 | Neural Machine Translation by Jointly Learning to Align and Translate Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio · arXiv (Cornell University) | Sep 2014 | 14,616 |
| 11 | Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc Viet Le · arXiv (Cornell University) | 10 Sep 2014 | 13,337 |
| 12 | Momentum Contrast for Unsupervised Visual Representation Learning Kaiming He, Haoqi Fan, Yuxin Wu et al. | Jun 2020 | 12,628 |
| 13 | Non-local Neural Networks Xiaolong Wang, Ross Girshick, Abhinav Gupta et al. | Jun 2018 | 11,465 |
| 14 | Fully Convolutional Networks for Semantic Segmentation Evan Shelhamer, Jonathan Long, Trevor J. Darrell · IEEE Transactions on Pattern Analysis and Machine Intelligence | 24 May 2016 | 11,276 |
| 15 | Path Aggregation Network for Instance Segmentation Shu Liu, Lu Qi, Haifang Qin et al. | Jun 2018 | 9,270 |
| 16 | The Psychophysics Toolbox. David H. Brainard · PubMed | 1997 | 9,189 |
| 17 | Effective Approaches to Attention-based Neural Machine Translation Thang Luong, Hieu T. T. L. Pham, Christopher D. Manning | 2015 | 8,653 |
| 18 | Attention Is All You Need Ashish Vaswani, Noam Shazeer, Niki Jitendra Parmar et al. | 23 Aug 2025 | 7,660 |
| 19 | Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Kelvin Xu, Jimmy Lei Ba, Ryan Kiros et al. · arXiv (Cornell University) | 10 Feb 2015 | 7,513 |
| 20 | The Pascal Visual Object Classes Challenge: A Retrospective Mark Everingham, S. M. Ali Eslami, Luc Van Gool et al. · International Journal of Computer Vision | 24 Jun 2014 | 7,447 |
| 21 | A Simple Framework for Contrastive Learning of Visual Representations Ting Chen, Simon Kornblith, Mohammad Norouzi et al. · arXiv (Cornell University) | 13 Feb 2020 | 7,327 |
| 22 | A Comprehensive Survey on Transfer Learning Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan et al. · Proceedings of the IEEE | 7 Jul 2020 | 6,487 |
| 23 | Show and tell: A neural image caption generator Oriol Vinyals, Alexander Toshev, Samy Bengio et al. | Jun 2015 | 6,460 |
| 24 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn, Pieter Abbeel, Sergey Levine · arXiv (Cornell University) | 9 Mar 2017 | 5,777 |
| 25 | Long-term recurrent convolutional networks for visual recognition and description Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama et al. | Jun 2015 | 5,351 |
| 26 | Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations Ranjay Krishna, Yuke Zhu, Oliver Groth et al. · International Journal of Computer Vision | 6 Feb 2017 | 5,329 |
| 27 | Learning Transferable Visual Models From Natural Language Supervision Alec Radford, Jong Wook Kim, Chris Hallacy et al. · arXiv (Cornell University) | 26 Feb 2021 | 5,294 |
| 28 | Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering Peter Anderson, Xiaodong He, Chris Buehler et al. | Jun 2018 | 5,173 |
| 29 | Natural Language Processing (almost) from Scratch Ronan Collobert, Jason Weston, Léon Bottou et al. · arXiv (Cornell University) | 2 Mar 2011 | 5,163 |
| 30 | Deep visual-semantic alignments for generating image descriptions Andrej Karpathy, Li Fei-Fei | Jun 2015 | 5,042 |
| 31 | EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks Mingxing Tan, Quoc Viet Le · arXiv (Cornell University) | 28 May 2019 | 5,011 |
| 32 | CIDEr: Consensus-based image description evaluation Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh | Jun 2015 | 4,861 |
| 33 | CSPNet: A New Backbone that can Enhance Learning Capability of CNN Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu et al. | Jun 2020 | 4,848 |
| 34 | Survey of Hallucination in Natural Language Generation Ziwei Ji, Nayeon Lee, Rita Frieske et al. · ACM Computing Surveys | 17 Nov 2022 | 4,501 |
| 35 | Deep Reinforcement Learning: A Brief Survey Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage et al. · IEEE Signal Processing Magazine | Nov 2017 | 4,470 |
| 36 | VQA: Visual Question Answering Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu et al. | Dec 2015 | 4,434 |
| 37 | GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Wang, Amanpreet Singh, Julian Michael et al. | 2018 | 4,101 |
| 38 | Learning Deconvolution Network for Semantic Segmentation Hyeonwoo Noh, Seunghoon Hong, Bohyung Han | Dec 2015 | 4,077 |
| 39 | Residual Attention Network for Image Classification Fei Wang, Mengqing Jiang, Chen Qian et al. | Jul 2017 | 3,806 |
| 40 | Representation Learning with Contrastive Predictive Coding Aäron van den Oord, Yazhe Li, Oriol Vinyals · arXiv (Cornell University) | 10 Jul 2018 | 3,744 |
| 41 | METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments Satanjeev Banerjee, Alon Lavie | Jun 2005 | 3,711 |
| 42 | Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers Sixiao Zheng, Jiachen Lu, Hengshuang Zhao et al. | Jun 2021 | 3,679 |
| 43 | DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition Jeff Donahue, Yangqing Jia, Oriol Vinyals et al. · arXiv (Cornell University) | 6 Oct 2013 | 3,557 |
| 44 | A large annotated corpus for learning natural language inference Samuel R. Bowman, Gabor Angeli, Christopher E. Potts et al. | 2015 | 3,497 |
| 45 | Exploring Simple Siamese Representation Learning Xinlei Chen, Kaiming He | Jun 2021 | 3,472 |
| 46 | Deep CORAL: Correlation Alignment for Deep Domain Adaptation Baochen Sun, Kate Saenko · Lecture notes in computer science | 2016 | 3,324 |
| 47 | Scene Parsing through ADE20K Dataset Bolei Zhou, Hang Zhao, Xavier Puig et al. | Jul 2017 | 3,321 |
| 48 | Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader et al. | Mar 2018 | 3,262 |
| 49 | Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks Maxime Oquab, Léon Bottou, Ivan Laptev et al. | Jun 2014 | 3,213 |
| 50 | Continual lifelong learning with neural networks: A review German Ignacio Parisi, Ronald Kemker, Jose L. Part et al. · Neural Networks | 10 Feb 2019 | 3,189 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.