---
title: "Incorporating Global Visual Features into Attention-Based Neural"
description: "Calixto, Liu and Campbell investigate whether a single global image representation can improve… 50 references, cited by 62 papers here."
authors: ["Iacer Calixto", "Qun Liu", "Nick Campbell"]
year: 2017
venue: "arXiv:1701.06521"
source: "https://arxiv.org/abs/1701.06521"
doi: "10.48550/arxiv.1701.06521"
arxiv: "1701.06521"
licence: "arXiv — non-exclusive license 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/calixto-2017-incorporating-global-visual-features-attention/"
---
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# Incorporating Global Visual Features into Attention-Based Neural Machine Translation

**Authors:** [Iacer Calixto](https://md.research.elamur.ai/free/en/authors/iacer-calixto/), [Qun Liu](https://md.research.elamur.ai/free/en/authors/qun-liu/) and [Nick Campbell](https://md.research.elamur.ai/free/en/authors/nick-campbell/) · **Year:** 2017 · **Venue:** [arXiv:1701.06521](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2017](https://md.research.elamur.ai/free/en/venues/emnlp/2017/)) · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1701.06521) · [PDF](https://arxiv.org/pdf/1701.06521) · [DOI 10.48550/arxiv.1701.06521](https://doi.org/10.48550/arxiv.1701.06521) · [arXiv 1701.06521](https://arxiv.org/abs/1701.06521) · [OpenAlex](https://openalex.org/W2581101319)

**Citations:** 155 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/calixto-2017-incorporating-global-visual-features-attention/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Calixto, Liu and Campbell investigate whether a single global image representation can improve attention-based neural machine translation of image descriptions from English into German. They start from a Bahdanau-style NMT system with a bidirectional GRU encoder. Into this system they feed a 4096-dimensional FC7 vector produced by a pre-trained VGG19 network, using one of three strategies. The vector is either projected into the embedding space and treated as extra source words, used to initialise the encoder state, or added to the decoder's initial state. The models are trained on Multi30k. The authors also test 145k synthetic sentence triples created by back-translating German captions from the comparable portion of the corpus. Initialising the encoder or the decoder with the image works better than the words-based approach. With the synthetic data, these variants beat a text-only NMT model, a PBSMT system and the multi-modal model of Huang et al. (2016) on every metric reported. The authors state that no purely neural system had previously surpassed PBSMT on all metrics for this dataset.

## Key points
- **Image features:** These are the 4096D FC7 activations of VGG19, which was pre-trained on ImageNet. The models are IMG 2W (image as source words), IMG E (encoder initialisation) and IMG D (decoder initialisation).
- **Rejected design:** Supplying image features to the decoder at every time step caused overfitting and blocked learning. This agrees with the earlier observation of Vinyals et al. (2015).
- **Results with back-translated data on the M30k T test set:**

  | Model | BLEU4 | METEOR | TER | chrF3 |
  |---|---|---|---|---|
  | PBSMT | 34.0 | 55.0 | 44.7 | 68.0 |
  | Text-only NMT | 35.5 | 53.4 | 43.3 | 65.3 |
  | IMG E | 38.5 | 55.7 | 41.4 | 68.3 |
  | IMG D | 38.5 | 55.9 | 41.6 | 68.4 |

- **Effect of synthetic data:** Without the 145k back-translated triples, BLEU was 36.9 for IMG 2W, 37.1 for IMG E and 37.3 for IMG D. Adding the triples helped IMG E and IMG D but gave no benefit to IMG 2W.
- **chrF3:** IMG E and IMG D were the only models that consistently outscored PBSMT on chrF3, a character-level metric known to correlate well with human judgements. The images-as-words approach, as used by Huang et al. (2016), performed worse.

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

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## Cite

Cite as: Iacer Calixto, Qun Liu and Nick Campbell. 2017. Incorporating Global Visual Features into Attention-Based Neural Machine Translation. arXiv:1701.06521.

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

```bibtex
@misc{calixto2017incorporating,
  title = {{Incorporating Global Visual Features into Attention-Based Neural Machine Translation}},
  author = {Iacer Calixto, Qun Liu and Nick Campbell},
  year = {2017},
  doi = {10.48550/arxiv.1701.06521},
  eprint = {1701.06521},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1701.06521}
}
```

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*Updated 2 November 2026*