The DOI matches its Crossref record; 37 of the 40 references it cites were found in a registry or an index.
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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In the citing paper
“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)
In the citing paper
“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)
In the citing paper
“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)
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
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
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
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
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
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
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
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
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
Flagged first, then by citations
3 of the 40 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- kuznetsova, 2014, Treetalk: Composition and compression of trees for image descriptions, ACL, 2
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Items 1,001–1,039 of 1,039
Progressively Parsing Interactional Objects for Fine Grained Action Detection
Ni, B., Yang, X., & Gao, S. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.116 · Cited by 69
Audio Caption: Listen and Tell
Wu, M., Dinkel, H., & Yu, K. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682377 · Cited by 52
Manufacturing an Artificial Intelligence Revolution
Katz, Y. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3078224 · Cited by 53
Multi-task paired masking with alignment modeling for medical vision-language pre-training
Zhang, K., Yang, Y., Yu, J., Jiang, H., Fan, J., Huang, Q., & Han, W. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3325965 · Cited by 51
“With reference to the latest progress in computer vision and machine translation, [14] took the lead in proposing an end-to-end encoder-decoder architecture for image captioning.”
BackgroundMethodMemory Manipulations in Extended Reality
Bonnail, E., Tseng, W.-J., Mcgill, M., Lecolinet, E., Huron, S., & Gugenheimer, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580988 · Cited by 51
VisualWebArena: evaluating multimodal agents on realistic visual web tasks
Koh, J. Y., Lo, R., Jang, L., Duvvur, V., Lim, M., Huang, P.-Y., Neubig, G., Zhou, S., Salakhutdinov, R., & Fried, D. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2024.acl-long.50 · Cited by 50
Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network
Li, Y., Pan, Y., Yao, T., Chen, J., & Mei, T. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17034 · Cited by 49
Image captioning by diffusion models: a survey
Daneshfar, F., Bartani, A., & Lotfi, P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109288 · Cited by 49
Mitigating Gender Bias in Captioning Systems
Tang, R., Du, M., Li, Y., Liu, Z., Zou, N., & Hu, X. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449950 · Cited by 50
Lin, Z., Zhang, D., Shi, D., Xu, R., Tao, Q., Wu, L., He, M., & Ge, Z. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104281 · Cited by 39
Multimodal Deep Learning using Images and Text for Information Graphic Classification
Kim, E., & McCoy, K. F. (2018) · Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3234695.3236357 · Cited by 39
GilBERT: Generative Vision-Language Pre-Training for Image-Text Retrieval
Hong, W., Ji, K., Liu, J., Wang, J., Chen, J., & Chu, W. (2021) · Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3404835.3462838 · Cited by 37
COOKIE: Contrastive Cross-Modal Knowledge Sharing Pre-training for Vision-Language Representation
Wen, K., Xia, J., Huang, Y., Li, L., Xu, J., & Shao, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00221 · Cited by 37
Two Sides of the Same Coin: White-box and Black-box Attacks for Transfer Learning
Zhang, Y., Song, Y., Liang, J., Bai, K., & Yang, Q. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403349 · Cited by 29
MusCaps: Generating Captions for Music Audio
Manco, I., Benetos, E., Quinton, E., & Fazekas, G. (2021) · 2021 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533461 · Cited by 36
“Our work is inspired by CNN-RNN architectures developed for image and audio captioning, but focusses on how such approaches can be extended to the music domain for the first time.”
BackgroundMethodInfluentialVisual Question Generation: The State of the Art
Patil, C., & Patwardhan, M. (2021) · ACM Computing Surveys · doi:10.1145/3383465 · Cited by 25
Alsharid, M., Cai, Y., Sharma, H., Drukker, L., Papageorghiou, A. T., & Noble, J. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102630 · Cited by 25
Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
Yan, B., Liu, R., Kuo, D., Adithan, S., Reis, E., Kwak, S., Venugopal, V., O’Connell, C., Saenz, A., Rajpurkar, P., et al. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.977 · Cited by 22
Abdulgalil, H. D., & Basir, O. A. (2025) · Natural Language Processing Journal · doi:10.1016/j.nlp.2025.100159 · Cited by 20
WebCode2M: A Real-World Dataset for Code Generation from Webpage Designs
Gui, Y., Li, Z., Wan, Y., Shi, Y., Zhang, H., Chen, B., Su, Y., Chen, D., Wu, S., Zhou, X., et al. (2025) · Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3696410.3714889 · Cited by 17
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Yang, S., Niu, J., Wu, J., & Liu, X. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-60248-2_48 · Cited by 17
Analysis of Tweets for Cyberbullying Detection
Mathur, S. A., Isarka, S., Dharmasivam, B., & C. D., J. (2023) · 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) · doi:10.1109/icsccc58608.2023.10176416 · Cited by 12
What BERT Sees: Cross-Modal Transfer for Visual Question Generation
Scialom, T., Bordes, P., Dray, P.-A., Staiano, J., & Gallinari, P. (2020) · Proceedings of the 13th International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.39 · Cited by 9
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Sharif, N., Nadeem, U., Shah, S. A. A., Bennamoun, M., & Liu, W. (2020) · Learning and Analytics in Intelligent Systems · doi:10.1007/978-3-030-49724-8_2 · Cited by 9
Li, Y., Kong, C., Zhao, G., & Zhao, Z. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11337-0 · Cited by 7
“Notably, Vinyals et al. (2015) proposed a dual attention mechanism combining bottom-up and top-down processes.”
Generating Natural Questions from Images for Multimodal Assistants
Patel, A., Bindal, A., Kotek, H., Klein, C., & Williams, J. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9413599 · Cited by 6
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“In the last decade, advances in computer vision have enabled machines to provide such descriptions (e.g., [129]).”
Jung, C., Mehta, S., Kulkarni, A., Zhao, Y., & Kim, Y.-S. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114846 · Cited by 91
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“…Models Finally, our work builds off advances in vision-language models (VLMs), used for many multimodal tasks such as image captioning (Vinyals et al., 2015), visual question answering (Antol et al., 2015), and other benchmarks (Mialon et al., 2023; Yue et al., 2023; Tong et al.,…”
Improved regularization of convolutional neural networks with cutout
DeVries, T., & Taylor, G. W. (2017) · arXiv · doi:10.48550/arxiv.1708.04552 · Cited by 2,776
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Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv · doi:10.48550/arxiv.1605.05396 · Cited by 2,494
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Bengio, S., Vinyals, O., Jaitly, N., & Shazeer, N. (2015) · arXiv · doi:10.48550/arxiv.1506.03099 · Cited by 1,621
Self-citationMicrosoft COCO Captions: Data Collection and Evaluation Server
Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. L. (2015) · arXiv · doi:10.48550/arxiv.1504.00325 · Cited by 1,614
Semi-supervised Sequence Learning
Dai, A. M., & Le, Q. V. (2015) · arXiv · doi:10.48550/arxiv.1511.01432 · Cited by 994
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Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2015) · arXiv · doi:10.48550/arxiv.1511.03677 · Cited by 879
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Karpathy, A., Johnson, J., & Fei-Fei, L. (2015) · arXiv · doi:10.48550/arxiv.1506.02078 · Cited by 845
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Vendrov, I., Kiros, R., Fidler, S., & Urtasun, R. (2015) · arXiv · doi:10.48550/arxiv.1511.06361 · Cited by 296
Evaluation of Text Generation: A Survey
Celikyilmaz, A., Clark, E., & Gao, J. (2020) · arXiv · doi:10.48550/arxiv.2006.14799 · Cited by 207
Metadata
- 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
- 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
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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.
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