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
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
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
Self-citationBleu: a method for automatic evaluation of machine translation
Papineni, K. (2024) · doi:10.57702/ljteqmrb · Cited by 3,668
Recurrent Neural Network RegularizationPreprint
Zaremba, W., Sutskever, I., & Vinyals, O. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1409.2329 · Cited by 3,174
Self-citationGenerating Sequences With Recurrent Neural Networks
Graves, A. (2013) · arXiv · doi:10.48550/arxiv.1308.0850 · Cited by 2,894
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
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
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 1,496
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
Self-citationFraming 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 1,375
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
Im2Text: Describing Images Using 1 Million Captioned Photographs
Ordóñez, V., Kulkarni, G. A., & Berg, T. L. (2011) · Cited by 879
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 825
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 794
Multimodal Neural Language Models
Kiros, R., Salakhutdinov, R., & Zemel, R. (2014) · Cited by 593
Collecting Image Annotations Using Amazon's Mechanical Turk
Rashtchian, C., Young, P., Hodosh, M., & Hockenmaier, J. (2010) · Cited by 541
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) · 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.2011.5995466 · Cited by 530
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 458
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 418
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 356
Collective Generation of Natural Image Descriptions
Кузнецова, П., Ordóñez, V., Berg, A., Berg, T. L., & Choi, Y. (2012) · Cited by 320
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 314
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 288
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
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
Generating Image Descriptions Using Dependency Relational Patterns
Aker, A., & Gaizauskas, R. (2010) · Cited by 85
Gerber, R., & Nagel, N.-H. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1996.561027 · Cited by 40
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
Cited by
Sorted by citations
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
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
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
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
“…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
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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