Multimodal intelligence: Representation learning, information fusion, and applications

Deep Residual Learning for Image Recognition

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Attention Is All You Need

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ImageNet classification with deep convolutional neural networks

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Long Short-Term Memory

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Deep learning

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MizAR 60 for Mizar 50

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet: A large-scale hierarchical image database

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Gradient-based learning applied to document recognition

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

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Microsoft COCO: Common Objects in Context

Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works

ImageNet Large Scale Visual Recognition Challenge

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Glove: Global Vectors for Word Representation

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Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works

Learning representations by back-propagating errors

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Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works

Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works

Unpaired image-to-image translation using cycle-consistent adversarial networks

Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602 · see citing works

Generative Adversarial Nets

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Reducing the dimensionality of data with neural networks

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GAN（Generative Adversarial Nets）

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Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

Sequence to Sequence Learning with Neural Networks

Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Deep Contextualized Word Representations

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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

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Enriching Word Vectors with Subword Information

Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645 · see citing works

Conditional Generative Adversarial Nets

Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863 · see citing works

Effective Approaches to Attention-based Neural Machine Translation

Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382 · see citing works

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365 · see citing works

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248 · see citing works

Learning Deep Architectures for AI

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Two-Stream Convolutional Networks for Action Recognition in Videos

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1406.2199 · Cited by 6,756 · see citing works

Show and tell: A neural image caption generator

Vinyals, O., Toshev, A., Bengio, S., & Erhan, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298935 · Cited by 6,318 · see citing works

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319 · see citing works

Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering

Anderson, P., He, X., Buehler, C., Teney, D., Johnson, M., Gould, S. J., & Zhang, L. (2018) · 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.2018.00636 · Cited by 5,117 · see citing works

Neural Architecture Search with Reinforcement LearningPreprint

Zoph, B., & Le, Q. V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.01578 · Cited by 5,091 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works

WaveNet: a generative model for raw audio

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Deep visual-semantic alignments for generating image descriptions

Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902 · see citing works

VQA: Visual Question Answering

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Some Mathematical Notes on Three-Mode Factor Analysis

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

Hudson, D. A., & Manning, C. D. (2019) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2019.00686 · Cited by 3,703 · see citing works

Front-End Factor Analysis for Speaker Verification

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Deep Learning: Methods and Applications

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StackGAN: Text to Photo-Realistic Image Synthesis with Stacked Generative Adversarial Networks

Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.629 · Cited by 3,021 · see citing works

Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition

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Conditional Image Synthesis With Auxiliary Classifier GANsPreprint

Odena, A., Olah, C., & Shlens, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.09585 · Cited by 2,968 · see citing works

Linguistic Regularities in Continuous Space Word Representations

Mikolov, T., Yih, W.-T., & Zweig, G. (2013) · Cited by 2,913 · see citing works

Generating Sequences With Recurrent Neural Networks

Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894 · see citing works

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A neural probabilistic language model

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Phoneme recognition using time-delay neural networks

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Microsoft COCO: Common Objects in Context

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Generative Adversarial Text to Image Synthesis

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Film: Visual reasoning with a general conditioning layer

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Deep Generative Image Models using a Laplacian Pyramid of Adversarial NetworksPreprint

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LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Tan, H., & Bansal, M. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1514 · Cited by 2,319 · see citing works

DeViSE: A Deep Visual-Semantic Embedding Model

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VoxCeleb2: Deep Speaker Recognition

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MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition

Guo, Y., Zhang, L., Hu, Y., He, X., & Gao, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46487-9_6 · Cited by 2,122 · see citing works

ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks

Lu, J., Batra, D., Parikh, D., & Lee, S. (2019) · arXiv · doi:10.48550/arxiv.1908.02265 · Cited by 2,109 · see citing works

Tensor Fusion Network for Multimodal Sentiment Analysis

Zadeh, A., Chen, M., Poria, S., Cambria, E., & Morency, L.-P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1115 · Cited by 2,102 · see citing works

Learning deep structured semantic models for web search using clickthrough data

Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. P. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505665 · Cited by 2,054 · see citing works

Stacked Attention Networks for Image Question Answering

Yang, Z., He, X., Gao, J., Deng, L., & Smola, A. J. (2016) · 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.2016.10 · Cited by 2,036 · see citing works

Progressive Neural Architecture Search

Liu, C., Zoph, B., Neumann, M., Shlens, J., Wei, H., Li, L.-J., Fei-Fei, L., Yuille, A., Huang, J., & Murphy, K. P. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01246-5_2 · Cited by 1,946 · see citing works

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00143 · Cited by 1,923 · see citing works

Image Captioning with Semantic Attention

You, Q., Jin, H., Wang, Z., Chen, F., & Luo, J. (2016) · 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.2016.503 · Cited by 1,859 · see citing works

Deep Neural Networks for Acoustic Modeling in Speech Recognition

Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., et al. (2012) · Cited by 1,839 · see citing works

MSR-VTT: A Large Video Description Dataset for Bridging Video and Language

Xu, J., Mei, T., Yao, T., & Rui, Y. (2016) · 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.2016.571 · Cited by 1,810 · see citing works

Deep Learning: Methods and Applications

Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,785 · see citing works

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Zitnick, C. L., & Girshick, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.215 · Cited by 1,781 · see citing works

A Structured Self-attentive Sentence Embedding

Lin, Z., Feng, M., Santos, C. N. D., Yu, M., Xiang, B., Zhou, B., & Bengio, Y. (2017) · arXiv · doi:10.48550/arxiv.1703.03130 · Cited by 1,743 · cited 1× · see citing works

Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning

Lu, J., Xiong, C., Parikh, D., & Socher, R. (2017) · 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.2017.345 · Cited by 1,724 · see citing works

Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding

Fukui, A., Park, D. H., Yang, D., Rohrbach, A., Darrell, T., & Rohrbach, M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1044 · Cited by 1,582 · see citing works

Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models

Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., & Lazebnik, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.303 · Cited by 1,444 · see citing works

Stacked Cross Attention for Image-Text Matching

Lee, K.-H., Chen, X., Hua, G., Hu, H., & He, X. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01225-0_13 · Cited by 1,402 · see citing works

Unifying Visual-Semantic Embeddings with Multimodal Neural Language ModelsPreprint

Kiros, R., Salakhutdinov, R., & Zemel, R. S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.2539 · Cited by 1,386 · see citing works

Finding frequent items in data streams

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From captions to visual concepts and back

Fang, H., Gupta, S., Iandola, F., Srivastava, R. K., Deng, L., Dollár, P., Gao, J., He, X., Mitchell, M., Platt, J. C., et al. (2015) · 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.2015.7298754 · Cited by 1,305 · see citing works

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 1,303 · see citing works

Skip-Thought Vectors

Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015) · arXiv · doi:10.48550/arxiv.1506.06726 · Cited by 1,293 · see citing works

New types of deep neural network learning for speech recognition and related applications: an overview

Deng, L., Hinton, G. E., & Kingsbury, B. E. D. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639344 · Cited by 1,222 · see citing works

StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks

Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., & Metaxas, D. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2856256 · Cited by 1,222 · see citing works

Multi-Task Deep Neural Networks for Natural Language Understanding

Liu, X., He, P., Chen, W., & Gao, J. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1441 · Cited by 1,216 · see citing works

VideoBERT: A Joint Model for Video and Language Representation Learning

Sun, C., Myers, A., Vondrick, C., Murphy, K. P., & Schmid, C. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00756 · Cited by 1,212 · see citing works

VisualBERT: A Simple and Performant Baseline for Vision and Language

Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., & Chang, K.-W. (2019) · arXiv · doi:10.48550/arxiv.1908.03557 · Cited by 1,198 · see citing works

Vision-and-Language Navigation: Interpreting Visually-Grounded Navigation Instructions in Real Environments

Anderson, P., Wu, Q., Teney, D., Bruce, J., Johnson, M., Sunderhauf, N., Reid, I., Gould, S., & van den Hengel, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00387 · Cited by 1,187 · see citing works

Efficient Low-rank Multimodal Fusion With Modality-Specific Factors

Liu, Z., Shen, Y., Lakshminarasimhan, V. B., Liang, P. P., Bagher Zadeh, A., & Morency, L.-P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1209 · Cited by 1,170 · see citing works

DenseCap: Fully Convolutional Localization Networks for Dense Captioning

Johnson, J., Karpathy, A., & Fei-Fei, L. (2016) · 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.2016.494 · Cited by 1,161 · see citing works

An audio-visual corpus for speech perception and automatic speech recognition

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ReferItGame: Referring to Objects in Photographs of Natural Scenes

Kazemzadeh, S., Ordóñez, V., Matten, M., & Berg, T. L. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1086 · Cited by 1,082 · see citing works

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Deep neural networks for small footprint text-dependent speaker verification

Variani, E., Lei, X., McDermott, E., Moreno, I. L., & Gónzalez-Domínguez, J. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6854363 · Cited by 1,073 · see citing works

Multimodal Learning with Deep Boltzmann Machines

Srivastava, N., & Salakhutdinov, R. (2012) · doi:10.5555/2627435.2697059 · Cited by 1,070 · see citing works

Neural Turing Machines

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Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)Preprint

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