Show and tell: A neural image caption generator
Abstract
Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.
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Vinyals et al. (2015) coupled a CNN for image encoding with an LSTM for textual encoding.
“They coupled a convolutional neural network for encoding of the image, with a recurrent neural network - specifically a long-short term memory net (LSTM) (Hochreiter and Schmidhuber (1997)) - for textual encoding.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Vinyals et al. (2015)
Vinyals et al. (2015) used human-generated sentences as ground truth for training and the BLEU metric for evaluation.
“They used human-generated sentences as ground truth for training, and used the bilingual evaluation understudy (BLEU) metric for evaluation.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Vinyals et al. (2015)
Vinyals et al. (2015) provided textual explanation for images using an end-to-end image captioning framework.
“Vinyals et al. (2015) provided textual explanation for images using an end-to-end image captioning framework.”
From Explainable artificial intelligence (XAI) in deep learning-based medical image analysis (2022) · cites this paper as Vinyals et al. (2015)
An image and a natural language caption can capture complimentary but converging information about a scene.
“Similarly, an image and a natural language caption can capture complimentary but converging information about a scene [32, 35].”
From Multimodal Generative Models for Scalable Weakly-Supervised Learning (2018) · cites this paper as [32]
Image Captioning depends on complex datasets including images and textual annotations.
“Dependence on complex datasets including images and textual annotations”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [43]
Image Captioning's semantic relationship between images and textual descriptions is intuitive, even for non-experts.
“Semantic relationship between images and textual descriptions is intuitive, even for nonexperts”
From Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches (2023) · cites this paper as [43]
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
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
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818 · 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
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 · see citing works
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344 · 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
Self-citationNeural 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
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845 · see citing works
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019 · see citing works
Self-citationBleu: a method for automatic evaluation of machine translation
Papineni, K. (2024) · doi:10.57702/ljteqmrb · Cited by 3,682 · see citing works
Recurrent Neural Network RegularizationPreprint
Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv.org · doi:10.48550/arxiv.1409.2329 · Cited by 3,086 · see citing works
Self-citationGenerating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894 · see citing works
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works
Young, P., Lai, A., Hodosh, M., & Hockenmaier, J. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00166 · Cited by 2,439 · 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. (2014) · arXiv · doi:10.48550/arxiv.1409.4842 · Cited by 1,461 · see citing works
Self-citationUnifying 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
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., & Hockenmaier, J. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3994 · Cited by 1,349 · see citing works
Every Picture Tells a Story: Generating Sentences from Images
Farhadi, A., Hejrati, M., Sadeghi, M. A., Young, P., Rashtchian, C., Hockenmaier, J., & Forsyth, D. A. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15561-1_2 · Cited by 1,240 · see citing works
Im2Text: Describing Images Using 1 Million Captioned Photographs
Ordóñez, V., Kulkarni, G. A., & Berg, T. L. (2011) · Cited by 879 · see citing works
Deep Fragment Embeddings for Bidirectional Image Sentence MappingPreprint
Karpathy, A., Joulin, A., & Fei-Fei, L. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.5679 · Cited by 802 · see citing works
Grounded Compositional Semantics for Finding and Describing Images with Sentences
Socher, R., Karpathy, A., Le, Q. V., Manning, C. D., & Ng, A. Y. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00177 · Cited by 779 · see citing works
Multimodal Neural Language Models
Kiros, R., Salakhutdinov, R., & Zemel, R. (2014) · Cited by 593 · see citing works
Collecting Image Annotations Using Amazon's Mechanical Turk
Rashtchian, C., Young, P., Hodosh, M., & Hockenmaier, J. (2010) · Cited by 541 · see citing works
Baby talk: Understanding and generating simple image descriptions
Kulkarni, G. A., Premraj, V., Dhar, S., Li, S., Choi, Y., Berg, A. C., & Berg, T. L. (2011) · doi:10.1109/cvpr.2011.5995466 · Cited by 521 · see citing works
Explain Images with Multimodal Recurrent Neural NetworksPreprint
Mao, J., Xu, W., Yang, Y., Wang, J., & Yuille, A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1410.1090 · Cited by 423 · see citing works
Midge: Generating Image Descriptions From Computer Vision Detections
Mitchell, M., Dodge, J., Goyal, A., Yamaguchi, K., Stratos, K., Han, X., Mensch, A., Berg, A., Berg, T. L., & Daumé, H. (2012) · Cited by 421 · see citing works
Composing Simple Image Descriptions using Web-scale N-grams
Li, S., Kulkarni, G. A., Berg, T. L., Berg, A. C., & Choi, Y. (2011) · Cited by 364 · see citing works
Collective Generation of Natural Image Descriptions
Кузнецова, П., Ordóñez, V., Berg, A., Berg, T. L., & Choi, Y. (2012) · Cited by 320 · see citing works
I2T: Image Parsing to Text Description
Yao, B., Yang, X., Lin, L., Lee, M. W., & Zhu, S. C. (2010) · Proceedings of the IEEE · doi:10.1109/jproc.2010.2050411 · Cited by 310 · see citing works
Improving Image-Sentence Embeddings Using Large Weakly Annotated Photo Collections
Gong, Y., Wang, L., Hodosh, M., Hockenmaier, J., & Lazebnik, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_35 · Cited by 286 · see citing works
Image Description using Visual Dependency Representations
Elliott, D., & Keller, F. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1128 · Cited by 282 · see citing works
T ree T alk : Composition and Compression of Trees for Image Descriptions
Kuznetsova, P., Ordóñez, V., Berg, T. L., & Choi, Y. (2014) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00188 · Cited by 268 · see citing works
Generating Image Descriptions Using Dependency Relational Patterns
Aker, A., & Gaizauskas, R. (2010) · Cited by 85 · see citing works
Gerber, R., & Nagel, N.-H. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1996.561027 · Cited by 40 · see citing works
Flagged first, then by citations
3 of the 40 references couldn’t be matched to a known paper.
Cited by
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Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00716 · Cited by 9,982 · 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
Representation Learning with Contrastive Predictive Coding
Oord, A. V. D., Li, Y., & Vinyals, O. (2018) · arXiv · doi:10.48550/arxiv.1807.03748 · Cited by 6,242 · see citing works
“…example, when pre-training a model to do image classification, the induced features transfer reasonably well to other image classification domains, but also lack certain information such as color or the ability to count that are irrelevant for classification but relevant for e.g. image captioning [4].”
BackgroundMethodSelf-citationGrad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01228-7 · Cited by 6,909 · see citing works
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works
CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features
Yun, S., Han, D., Chun, S., Oh, S. J., Yoo, Y., & Choe, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00612 · Cited by 5,400 · see citing works
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
Multimodal Machine Learning: A Survey and Taxonomy
Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2798607 · Cited by 5,018 · see citing works
VQA: Visual Question Answering
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.279 · Cited by 4,487 · see citing works
Social LSTM: Human Trajectory Prediction in Crowded Spaces
Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (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.2016.110 · Cited by 3,584 · see citing works
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.369 · Cited by 4,616 · see citing works
CIDEr: Consensus-based image description evaluation
Vedantam, R., Zitnick, C. L., & Parikh, D. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299087 · Cited by 4,845 · see citing works
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Rawat, W., & Wang, Z. (2017) · Neural Computation · doi:10.1162/neco_a_00990 · Cited by 3,503 · see citing works
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00097 · Cited by 3,639 · see citing works
Transformers in Vision: A Survey
Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022) · ACM Computing Surveys · doi:10.1145/3505244 · Cited by 3,595 · see citing works
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921 · see citing works
Cyclical Learning Rates for Training Neural Networks
Smith, L. N. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.58 · Cited by 2,931 · see citing works
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806 · see citing works
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y. H., & Zhang, Y. (2017) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2017.2753802 · Cited by 2,678 · see citing works
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00240 · Cited by 2,466 · see citing works
Recurrent Neural Network RegularizationPreprint
Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv.org · doi:10.48550/arxiv.1409.2329 · Cited by 3,086 · see citing works
Self-citationSelf-Critical Sequence Training for Image Captioning
Rennie, S. J., Marcheret, E., Mroueh, Y., Ross, J., & Goel, V. (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.2017.131 · Cited by 2,177 · see citing works
A Critical Review of Recurrent Neural Networks for Sequence LearningPreprint
Lipton, Z. C., Berkowitz, J. A., & Elkan, C. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.00019 · Cited by 2,711 · see citing works
SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning
Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., & Chua, T. (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.2017.667 · Cited by 2,073 · see citing works
Generating Sentences from a Continuous Space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A., Jozefowicz, R., & Bengio, S. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1002 · Cited by 2,061 · see citing works
Self-citationStacked 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
Vinyals, O., Fortunato, M., & Jaitly, N. (2015) · neural information processing systems · Cited by 2,024 · see citing works
Self-citationSelf-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036 · see citing works
Conditional Prompt Learning for Vision-Language Models
Zhou, K., Yang, J., Loy, C. C., & Liu, Z. (2022) · 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/cvpr52688.2022.01631 · Cited by 1,918 · see citing works
“Interestingly, the paradigm of CoCoOp is analogous to image captioning [49], which explains why instance-conditional prompts are more generalizable: they are optimized to characterize each instance (more robust to class shift) rather than to serve only for some specific classes .”
BackgroundConceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
Sharma, P., Ding, N., Goodman, S., & Soricut, R. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1238 · Cited by 1,879 · see citing works
Unsupervised Learning of Video Representations using LSTMs
Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861 · 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
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
Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.670 · Cited by 2,287 · 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
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Serban, I. V., Sordoni, A., Bengio, Y., Courville, A. C., & Pineau, J. (2016) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v30i1.9883 · Cited by 1,639 · see citing works
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Rohrbach, M., Venugopalan, S., Guadarrama, S., Saenko, K., & Darrell, T. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2599174 · Cited by 1,547 · see citing works
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works
Sequence to Sequence -- Video to Text
Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R., Darrell, T., & Saenko, K. (2015) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2015.515 · Cited by 1,507 · 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
Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400 · see citing works
Self-citationA Survey of Deep Learning-Based Object Detection
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019) · IEEE Access · doi:10.1109/access.2019.2939201 · Cited by 1,392 · see citing works
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
Liu, J., Shahroudy, A., Xu, D., & Wang, G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46487-9_50 · Cited by 1,353 · see citing works
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328 · see citing works
Zhang, Y., Xiong, R., He, H., & Pecht, M. G. (2018) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2018.2805189 · Cited by 1,326 · see citing works
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics
Hodosh, M., Young, P., & Hockenmaier, J. (2013) · Journal of Artificial Intelligence Research · doi:10.1613/jair.3994 · Cited by 1,349 · see citing works
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
Deep Learning in Medical Imaging: General Overview
Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017) · Korean Journal of Radiology · doi:10.3348/kjr.2017.18.4.570 · Cited by 1,287 · see citing works
CNN-RNN: A Unified Framework for Multi-label Image Classification
Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., & Xu, W. H. (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.2016.251 · Cited by 1,326 · see citing works
Generation and Comprehension of Unambiguous Object Descriptions
Mao, J., Huang, J., Toshev, A., Camburu, O., Yuille, A., & Murphy, K. P. (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.2016.9 · Cited by 1,211 · see citing works
Self-citationMeshed-Memory Transformer for Image Captioning
Cornia, M., Stefanini, M., Baraldi, L., & Cucchiara, R. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01059 · Cited by 1,259 · see citing works
Recent Trends in Deep Learning Based Natural Language ProcessingPreprint
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2017) · IEEE Computational Intelligence Magazine · doi:10.48550/arxiv.1708.02709 · Cited by 3,133 · see citing works
SPICE: Semantic Propositional Image Caption Evaluation
Anderson, P., Fernando, B., Johnson, M., & Gould, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_24 · Cited by 1,986 · see citing works
Wang, Y., Ma, F., Jin, Z., Yuan, Y., Xun, G., Jha, K., Su, L., & Gao, J. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219903 · Cited by 1,181 · see citing works
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
Li, Z., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., & Zhong, Y. (2018) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2018.23158 · Cited by 1,174 · 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
Deep multimodal learning: a survey on recent advances and trends
Ramachandram, D., & Taylor, G. W. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2738401 · Cited by 1,139 · see citing works
Convolutional neural network: a review of models, methodologies and applications to object detection
Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119 · see citing works
Neural Motifs: Scene Graph Parsing with Global Context
Zellers, R., Yatskar, M., Thomson, S., & Choi, Y. (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.2018.00611 · Cited by 1,095 · see citing works
Attention on Attention for Image Captioning
Huang, L., Wang, W., Chen, J., & Wei, X.-Y. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00473 · Cited by 1,092 · see citing works
Deep Visual-Semantic Alignments for Generating Image Descriptions
Karpathy, A., & Fei-Fei, L. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2598339 · Cited by 1,048 · see citing works
Pre-trained models: Past, present and future
Han, X., Zhang, Z., Ding, N., Gu, Y., Liu, X., Huo, Y., Qiu, J., Yao, Y., Zhang, A., Zhang, L., et al. (2021) · AI Open · doi:10.1016/j.aiopen.2021.08.002 · Cited by 994 · see citing works
Compressing Neural Networks with the Hashing Trick
Chen, W., Wilson, J. T., Tyree, S., Weinberger, K. Q., & Chen, Y. (2015) · arXiv · doi:10.48550/arxiv.1504.04788 · Cited by 974 · see citing works
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2014) · doi:10.21236/ada623249 · Cited by 1,081 · see citing works
Describing Videos by Exploiting Temporal Structure
Yao, L., Torabi, A., Cho, K., Ballas, N., Pal, C., Larochelle, H., & Courville, A. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.512 · Cited by 1,035 · see citing works
Exploring Visual Relationship for Image Captioning
Yao, T., Pan, Y., Li, Y., & Mei, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_42 · Cited by 962 · see citing works
Structured Sequence Modeling with Graph Convolutional Recurrent Networks
Seo, Y., Defferrard, M., Vandergheynst, P., & Bresson, X. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04167-0_33 · Cited by 942 · see citing works
3-D Deep Learning Approach for Remote Sensing Image Classification
Hamida, A. B., Benoît, A., Lambert, P., & Amar, C. B. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2818945 · Cited by 929 · see citing works
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
Zhang, P., Wang, D., Lu, H., Wang, H., & Ruan, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.31 · Cited by 913 · see citing works
Recurrent Network Models for Human Dynamics
Fragkiadaki, K., Levine, S., Felsen, P., & Malik, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.494 · Cited by 904 · see citing works
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879 · see citing works
“[23] VGGNet RNN VS,SL,WS,EDA Fang et al. 2015 [33] AlexNet, VGGNet MELM VS,SL,WS,CA Jia et al. 2015 [59] VGGNet LSTM VS,SL,WS,EDA Karpathy et al. 2015 [65] VGGNet RNN MS,SL,WS,EDA Vinyals et al. 2015 [142] GoogLeNet LSTM VS,SL,WS,EDA Xu et al. 2015 [152] AlexNet LSTM VS,SL,WS,EDA,AB Jin et al. 2015 [61] VGGNet LSTM VS…”
BackgroundMethodInfluentialAuto-Encoding Scene Graphs for Image Captioning
Yang, X., Tang, K., Zhang, H., & Cai, J. (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.2019.01094 · Cited by 852 · see citing works
Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs
Jin, Z., Cao, J., Guo, H., Zhang, Y., & Luo, J. (2017) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3123266.3123454 · Cited by 859 · see citing works
MVAE: Multimodal Variational Autoencoder for Fake News Detection
Khattar, D., Goud, J. S., Gupta, M., & Varma, V. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313552 · Cited by 848 · see citing works
Miikkulainen, R. P., Liang, J., Meyerson, E., Rawal, A., Fink, D., Francon, O., Raju, B., Shahrzad, H., Navruzyan, A., Duffy, N., et al. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-815480-9.00015-3 · Cited by 854 · see citing works
Sequence-Level Knowledge Distillation
Kim, Y., & Rush, A. M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1139 · Cited by 836 · see citing works
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · see citing works
“Visual recognition tasks involving language, such as captioning (Vinyals et al., 2015), visual question answering (Antol et al.”
BackgroundNeural Network Methods for Natural Language Processing
Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821 · see citing works
Flamingo: a visual language model for few-shot learning
Alayrac, J.-B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1723 · Cited by 966 · see citing works
“[116] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan.”
BackgroundResultSelf-citationVisual7W: Grounded Question Answering in Images
Zhu, Y., Groth, O., Bernstein, M., & Fei-Fei, L. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.540 · Cited by 794 · see citing works
“Pioneer work in image captioning [4, 5, 13, 45, 47], sentence-based image retrieval [14, 40] and visual QA [1, 7, 36] shows promising results.”
BackgroundToward Controlled Generation of TextPreprint
Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., & Xing, E. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00955 · Cited by 802 · see citing works
Learning Deep Representations of Fine-Grained Visual Descriptions
Reed, S., Akata, Z., Lee, H., & Schiele, B. (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.2016.13 · Cited by 802 · see citing works
A hybrid deep learning based traffic flow prediction method and its understanding
Wu, Y., Tan, H., Qin, L., Ran, B., & Jiang, Z. (2018) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2018.03.001 · Cited by 795 · see citing works
(2016) · doi:10.1007/978-3-319-46475-6_5 · Cited by 1,081 · see citing works
Learning Deep Structure-Preserving Image-Text Embeddings
Wang, L., Li, Y., & Lazebnik, S. (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.2016.541 · Cited by 788 · see citing works
Gatt, A., & Krahmer, E. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5477 · Cited by 776 · see citing works
The design and implementation of Xiaoice, an empathetic social chatbot
Zhou, L., Gao, J., Li, D., & Shum, H.-Y. (2020) · Computational Linguistics · doi:10.1162/coli_a_00368 · Cited by 777 · see citing works
Boosting Image Captioning with Attributes
Yao, T., Pan, Y., Li, Y., Qiu, Z., & Mei, T. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.524 · Cited by 752 · see citing works
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
Xu, H., & Saenko, K. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46478-7_28 · Cited by 753 · see citing works
Dual Attention Networks for Multimodal Reasoning and Matching
Nam, H., Ha, J.-W., & Kim, J. (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.2017.232 · Cited by 749 · see citing works
From Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749 · see citing works
“…(Krizhevsky et al., 2012; He et al., 2016), information retrieval (Huang et al., 2013; Elkahky et al., 2015), multimodal intelligence (Fang et al., 2015; Vinyals et al., 2015; Karpathy and Fei-fei, 2015; He and Deng, 2017), and empathic conversational systems (Fung et al., 2016; Alam et al., 2017).”
BackgroundDeep learning in medical imaging and radiation therapy
Sahiner, B., Pezeshk, A. X., Hadjiiski, L. M., Wang, X., Drukker, K., Cha, K. H., Summers, R. M., & Giger, M. L. (2018) · Medical Physics · doi:10.1002/mp.13264 · Cited by 739 · see citing works
MUTAN: Multimodal Tucker Fusion for Visual Question Answering
Ben-younes, H., Cadène, R., Cord, M., & Thome, N. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.285 · Cited by 733 · see citing works
X-Linear Attention Networks for Image Captioning
Pan, Y., Yao, T., Li, Y., & Mei, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01098 · Cited by 720 · see citing works
Exploring Models and Data for Remote Sensing Image Caption Generation
Lu, X., Wang, B., Zheng, X., & Li, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2776321 · Cited by 703 · see citing works
Generating Radiology Reports via Memory-driven Transformer
Chen, Z., Song, Y., Chang, T.-H., & Wan, X. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.112 · Cited by 693 · see citing works
MovieQA: Understanding Stories in Movies through Question-Answering
Tapaswi, M., Zhu, Y., Stiefelhagen, R., Torralba, A. B., Urtasun, R., & Fidler, S. (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.2016.501 · Cited by 646 · see citing works
MirrorGAN: Learning Text-To-Image Generation by Redescription
Qiao, T., Zhang, J., Xu, D., & Tao, D. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00160 · Cited by 644 · see citing works
“As shown in Figure 1 (a) and (c), if an image generated by T2I is semantically consistent with the given text description, its redescription by I2T should have exactly the same semantics with the given text description.”
BackgroundMethodInfluential
- Authors
- Vinyals, O., Toshev, A., Bengio, S., & Erhan, D.
- Affiliations
- Google (United States) (US)
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3156-3164
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1895577753
- Cited by
- 6,318
- References
- 40
- Citations by year
- 2026: 228 · 2025: 464 · 2024: 547 · 2023: 598 · 2022: 589 · 2021: 747 · 2020: 748 · 2019: 770 · 2018: 656 · 2017: 471 · 2016: 329 · 2015: 158 · 2014: 9 · 2013: 1 · 2012: 2
- FWCI
- 220.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- image captioning, recurrent neural networks, generative models, machine translation, image description generation, PASCAL dataset, Flickr30k, COCO dataset, BLEU evaluation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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