---
title: "Show and Tell — Vinyals 2014"
description: "The paper addresses automatic image captioning: writing a fluent English sentence that names the objects in a picture and also describes their…"
authors: ["Oriol Vinyals", "Alexander Toshev", "Samy Bengio", "Dumitru Erhan"]
year: 2014
venue: "arXiv:1411.4555"
source: "https://arxiv.org/abs/1411.4555"
doi: "10.48550/arxiv.1411.4555"
arxiv: "1411.4555"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/vinyals-2014-show-tell-neural-image-caption/"
---
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# Show and Tell: A Neural Image Caption Generator

**Authors:** [Oriol Vinyals](https://md.research.elamur.ai/free/en/authors/oriol-vinyals/), [Alexander Toshev](https://md.research.elamur.ai/free/en/authors/alexander-toshev/), [Samy Bengio](https://md.research.elamur.ai/free/en/authors/samy-bengio/), [Dumitru Erhan](https://md.research.elamur.ai/free/en/authors/dumitru-erhan/) · **Year:** 2014 · **Venue:** arXiv:1411.4555 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1411.4555) · [PDF](https://arxiv.org/pdf/1411.4555) · [DOI 10.48550/arxiv.1411.4555](https://doi.org/10.48550/arxiv.1411.4555) · [arXiv 1411.4555](https://arxiv.org/abs/1411.4555) · [OpenAlex](https://openalex.org/W2951912364)

**Citations:** 136 citations (openalex-snapshot · 2026-10-14) · [470](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses automatic image captioning: writing a fluent English sentence that names the objects in a picture and also describes their attributes, how they relate and what they are doing. Earlier systems either combined object detectors with templates and hand-written rules, or retrieved existing captions. The authors instead propose NIC (Neural Image Caption), a single network trained end to end with stochastic gradient descent. It borrows the encoder–decoder approach from neural machine translation but replaces the source-language RNN with a deep CNN. That CNN is pretrained for image classification and uses the batch-normalized architecture that topped ILSVRC 2014. The image vector it produces goes into an LSTM, which generates the caption one word at a time using learned word embeddings. Training maximizes the log-likelihood of the reference captions. On Pascal, Flickr8k, Flickr30k, SBU and MSCOCO, NIC clearly beats earlier caption generators on BLEU and does well on retrieval-style ranking, though human raters still prefer human-written captions. NIC showed that a general sequence model can learn to describe images directly from image–caption pairs, and it became a standard baseline in vision–language research.

## Key points
- **BLEU-1:** Pascal rises from 25 (previous best) to 59, against about 69 for humans. Flickr30k goes from 56 to 66 and SBU from 19 to 28. On MSCOCO, NIC reaches a state-of-the-art BLEU-4 of 27.7.
- **Human ratings (average scores):** COCO-1k 2.72, Pascal 2.45 and Flickr8k 2.37. A reference system scored 2.08 and ground-truth captions 3.89. This gap suggests BLEU makes NIC look closer to human quality than it is.
- **Ranking on Flickr8k:** NIC beats DeFrag, m-RNN and MNLM, with R@1 of 20 for annotation and 19 for search and median ranks of 6 and 5. On Flickr30k it falls behind MNLM, which was trained with a ranking-specific loss.
- **Novelty:** the top-scoring caption copies a training sentence 80% of the time. Among the 15 best candidates, roughly half are new sentences with similar BLEU scores.
- **Word embeddings:** the learned embeddings group related words together, for example horse near pony, donkey and mule, and car near van, cab and jeep.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

Topics: [Machine translation](https://md.research.elamur.ai/free/en/topics/machine-translation/)

## Cited by

Cited by

470 papers of our library cite it (of the 140673 whose bibliographies we read); 43 of them have a page here, 427 not yet.

- [Flamingo: a Visual Language Model for Few-Shot Learning](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
- [SPICE: Semantic Propositional Image Caption Evaluation](https://md.research.elamur.ai/free/en/papers/anderson-2016-spice-semantic-propositional-image-caption/)
- [An Actor-Critic Algorithm for Sequence Prediction](https://md.research.elamur.ai/free/en/papers/bahdanau-2016-actor-critic-algorithm-sequence-prediction/)
- [Generating Sentences from a Continuous Space](https://md.research.elamur.ai/free/en/papers/bowman-2016-generating-sentences-continuous-space/)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://md.research.elamur.ai/free/en/papers/chen-2015-microsoft-coco-captions-data-collection/)
- [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://md.research.elamur.ai/free/en/papers/chen-2022-pali-jointly-scaled-multilingual-language/)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://md.research.elamur.ai/free/en/papers/donahue-2014-long-term-recurrent-convolutional-networks/)
- [Multi30K: Multilingual English-German Image Descriptions](https://md.research.elamur.ai/free/en/papers/elliott-2016-multi30k-multilingual-english-german-image/)
- [Controllable Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/fan-2018-controllable-abstractive-summarization/)
- [Pragmatically Informative Image Captioning with Character-Level Inference](https://md.research.elamur.ai/free/en/papers/gordon-2018-pragmatically-informative-image-captioning-character/)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://md.research.elamur.ai/free/en/papers/goyal-2016-making-v-vqa-matter-elevating/)
- [A Deep Generative Framework for Paraphrase Generation](https://md.research.elamur.ai/free/en/papers/gupta-2017-deep-generative-framework-paraphrase-generation/)
- [Toward Controlled Generation of Text](https://md.research.elamur.ai/free/en/papers/hu-2017-toward-controlled-generation-text/)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://md.research.elamur.ai/free/en/papers/karpathy-2014-deep-visual-semantic-alignments-generating/)
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- [Generation and Comprehension of Unambiguous Object Descriptions](https://md.research.elamur.ai/free/en/papers/mao-2015-generation-comprehension-unambiguous-object-descriptions/)
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- [Representation Learning with Contrastive Predictive Coding](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
- [Neural Paraphrase Generation with Stacked Residual LSTM Networks](https://md.research.elamur.ai/free/en/papers/prakash-2016-neural-paraphrase-generation-stacked-residual/)
- [Self-critical Sequence Training for Image Captioning](https://md.research.elamur.ai/free/en/papers/rennie-2016-self-critical-sequence-training-image/)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://md.research.elamur.ai/free/en/papers/selvaraju-2016-grad-cam-visual-explanations-deep/)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://md.research.elamur.ai/free/en/papers/serban-2015-building-end-end-dialogue-systems/)
- [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://md.research.elamur.ai/free/en/papers/sharma-2018-conceptual-captions-cleaned-hypernymed-image/)
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- [CIDEr: Consensus-based Image Description Evaluation](https://md.research.elamur.ai/free/en/papers/vedantam-2014-cider-consensus-based-image-description/)
- [Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models](https://md.research.elamur.ai/free/en/papers/vijayakumar-2016-diverse-beam-search-decoding-diverse/)
- [A Neural Conversational Model](https://md.research.elamur.ai/free/en/papers/vinyals-2015-neural-conversational-model/)
- [Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models](https://md.research.elamur.ai/free/en/papers/wu-2023-visual-chatgpt-talking-drawing-editing/)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/)
- [Unsupervised Text Style Transfer using Language Models as Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2018-unsupervised-text-style-transfer-using/)
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- [Recurrent Neural Network Regularization](https://md.research.elamur.ai/free/en/papers/zaremba-2014-recurrent-neural-network-regularization/)
- [Know What You Don't Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories](https://md.research.elamur.ai/free/en/papers/zarrie-2019-know-what-you-don-t/)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://md.research.elamur.ai/free/en/papers/zhao-2017-men-also-like-shopping-reducing/)
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## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{vinyals2014show,
  title = {{Show and Tell: A Neural Image Caption Generator}},
  author = {Vinyals, Oriol and Toshev, Alexander and Bengio, Samy and Erhan, Dumitru},
  year = {2014},
  doi = {10.48550/arxiv.1411.4555},
  eprint = {1411.4555},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1411.4555}
}
```

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