---
title: "Modeling Context in Referring Expressions — Yu 2016"
description: "The paper addresses generating and comprehending referring expressions: phrases that single out one object in an image so a listener can find it."
authors: ["Licheng Yu", "Patrick Poirson", "Shan Yang", "Alexander C. Berg", "Tamara L. Berg"]
year: 2016
venue: "arXiv:1608.00272"
source: "https://arxiv.org/abs/1608.00272"
doi: "10.48550/arxiv.1608.00272"
arxiv: "1608.00272"
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/yu-2016-modeling-context-referring-expressions/"
---
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# Modeling Context in Referring Expressions

**Authors:** [Licheng Yu](https://md.research.elamur.ai/free/en/authors/licheng-yu/), Patrick Poirson, Shan Yang, [Alexander C. Berg](https://md.research.elamur.ai/free/en/authors/alexander-c-berg/), Tamara L. Berg · **Year:** 2016 · **Venue:** arXiv:1608.00272 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1608.00272) · [PDF](https://arxiv.org/pdf/1608.00272) · [DOI 10.48550/arxiv.1608.00272](https://doi.org/10.48550/arxiv.1608.00272) · [arXiv 1608.00272](https://arxiv.org/abs/1608.00272) · [OpenAlex](https://openalex.org/W2489434015)

**Citations:** 1018 citations (openalex-snapshot · 2026-10-14) · [147](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
The paper addresses generating and comprehending referring expressions: phrases that single out one object in an image so a listener can find it. Earlier deep models, notably Mao et al., used features of the whole image as context. The authors argue that this context is too generic, because ambiguity usually comes from other objects of the same category. They make two changes. First, they add visual comparison features ("visdif") that encode how the target differs from same-category objects in appearance (VGG-fc7 differences), location and size, and feed these into an LSTM. Second, they couple the LSTMs for all same-type objects in an image ("tie"), so the resulting expressions are complementary rather than duplicated. The models are evaluated on RefCOCO, RefCOCO+ and RefCOCOg against Baseline and MMI models. Visual comparison and joint generation both improve automatic scores and human-judged accuracy. They also sharply reduce cases where several objects receive identical descriptions. This matters for human–robot dialogue and grounded language systems, where an unambiguous reference is the whole point.

## Key points
- **Data and baselines:** The work uses three datasets built on MSCOCO (RefCOCO, RefCOCO+, RefCOCOg), released with a toolbox at github.com/lichengunc/refer. Baseline and MMI models are reimplemented following Mao et al.
- **Features and automatic metrics:** The models use VGG-fc7 features and a 5-d location/size vector. "visdif+tie" reaches the highest Bleu/Rouge/Meteor in almost every setting. MMI variants score lower on these automatic metrics.
- **Human evaluation:** Three Turkers clicked on the object each generated expression describes. On RefCOCO, the share of expressions resolved to the correct object rose from 62.42%/64.99% (Baseline, Test A/B) to 71.40%/76.14% (visdif+tie).
- **Duplicate descriptions:** The fraction of RefCOCO images with identical expressions for multiple objects fell from 15.60%/16.40% to 4.27%/5.33%. On RefCOCO+, "visdif+MMI+tie" reduced it from 28.67%/46.27% to 10.13%/13.33%.
- **RefCOCOg limitation:** Tied generation was not applied to RefCOCOg. In that dataset, objects from the same image can fall into different train/test splits.

*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/)

## Cited by

Cited by

147 papers of our library cite it (of the 140673 whose bibliographies we read); 16 of them have a page here, 131 not yet.

- [UNITER: UNiversal Image-TExt Representation Learning](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/)
- [MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
- [How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://md.research.elamur.ai/free/en/papers/chen-2024-far-gpt-4v-closing/)
- [Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models](https://md.research.elamur.ai/free/en/papers/deitke-2024-molmo-pixmo-open-weights-open/)
- [Referring Expressions with Rational Speech Act Framework: A Probabilistic Approach](https://md.research.elamur.ai/free/en/papers/le-2022-referring-expressions-rational-speech-act/)
- [TVQA: Localized, Compositional Video Question Answering](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://md.research.elamur.ai/free/en/papers/li-2024-llava-onevision-easy-visual-task/)
- [Kosmos-2: Grounding Multimodal Large Language Models to the World](https://md.research.elamur.ai/free/en/papers/peng-2023-kosmos-2-grounding-multimodal-large/)
- [Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/tong-2024-cambrian-1-fully-open-vision/)
- [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
- [CogVLM: Visual Expert for Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-cogvlm-visual-expert-pretrained/)
- [DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding](https://md.research.elamur.ai/free/en/papers/wu-2024-deepseek-vl2-mixture-experts/)
- [MiniCPM-V: A GPT-4V Level MLLM on Your Phone](https://md.research.elamur.ai/free/en/papers/yao-2024-minicpm-v-gpt-4v/)
- [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/)

## Cite

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

```bibtex
@misc{yu2016modeling,
  title = {{Modeling Context in Referring Expressions}},
  author = {Yu, Licheng and Poirson, Patrick and Yang, Shan and Berg, Alexander C. and Berg, Tamara L.},
  year = {2016},
  doi = {10.48550/arxiv.1608.00272},
  eprint = {1608.00272},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1608.00272}
}
```

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