---
title: "ReferItGame: Referring to Objects in — Kazemzadeh 2014"
description: "The paper studies referring expression generation (REG), which means producing phrases that single out one object in an image."
authors: ["Sahar Kazemzadeh", "Vicente Ordonez", "Mark Matten", "Tamara Berg"]
year: 2014
venue: "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/D14-1086/"
doi: "10.3115/v1/d14-1086"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/kazemzadeh-2014-referitgame-referring-objects-photographs-natural/"
---
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# ReferItGame: Referring to Objects in Photographs of Natural Scenes

**Authors:** Sahar Kazemzadeh, [Vicente Ordonez](https://md.research.elamur.ai/free/en/authors/vicente-ordonez/), Mark Matten, [Tamara Berg](https://md.research.elamur.ai/free/en/authors/tamara-berg/) · **Year:** 2014 · **Venue:** Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/D14-1086/) · [PDF](https://aclanthology.org/D14-1086.pdf) · [DOI 10.3115/v1/d14-1086](https://doi.org/10.3115/v1/d14-1086) · [OpenAlex](https://openalex.org/W2251512949)

**Citations:** 1048 citations (openalex-snapshot · 2026-10-14) · [228](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
The paper studies referring expression generation (REG), which means producing phrases that single out one object in an image. Earlier REG corpora used synthetic graphics or small objects placed on plain backgrounds. Here the setting is cluttered, real-world photographs. To get data cheaply and at scale, the authors built ReferItGame, a two-player online game. One player writes a description of an outlined object, and the other must click on it, so each successful round also checks the expression. Using images from the ImageCLEF dataset, the game produced 130,525 verified expressions for 96,654 objects in 19,894 photos. Their analysis shows that an object's category shapes how people describe it, including which attributes they mention, which nearby objects they use as anchors, and which category names they choose. Building on this, they propose an optimization model that decides jointly which attributes to mention and which values to give them. It combines visual estimates with category-specific priors. The model clearly beats a prior-only baseline on three test sets. The resource and analysis move REG toward realistic visual scenes.

## Key points
- Corpus size: 130,525 expressions referring to 96,654 distinct objects across 19,894 natural-scene photographs, drawn from ImageCLEF. Over 100k distinct games were created, one per labeled object.
- Expressions from rounds where the second player clicked the wrong object are released but left out of the analyses.
- Relative references, such as locating a car by where it sits next to a man, become more frequent as more objects of the same type appear in the image. The probability is 0.12 with one such object, 0.22 with two, and 0.26 with more.
- The generation model is solved as a binary integer linear program with IBM ILOG CPLEX. It uses unary priors that mix category-specific and global counts, plus pairwise potentials for attribute co-occurrence and for how well relative-location and relative-object words fit together.
- Evaluation uses three test sets of 500 objects each, with 3 manually attribute-labeled expressions per object. Human agreement is 72.31%. The full model reaches precision/recall of 36.28/53.44 (A), 36.68/59.80 (B) and 37.73/48.54 (C), compared with baselines of about 28–30% precision and 37–51% recall.

## Interesting ideas
- **Built-in verification through a second player:** an annotation is kept only if another person can act on it correctly. This removes a separate validation step and could be reused for collecting instructions, place descriptions, or paraphrases.
- **Canned partner when nobody is online:** a solo player is matched against pre-recorded sessions. Their text is compared to stored expressions using lemmatized bag-of-words cosine similarity, and the stored click is replayed. This cheap trick keeps a crowdsourcing game alive during low-traffic periods.
- **Prioritizing the hardest items first:** games were ordered so that objects sharing a category with others in the same image came first. Collection effort is focused where disambiguation is actually needed.
- **Category-conditioned priors with global backoff:** attribute usage is estimated per object category and smoothed toward dataset-wide counts. This is a simple way to capture domain-specific language habits without losing coverage for rare categories.
- **Stratified test sets:** the model was evaluated on a random sample, on a category-balanced sample of interesting categories, and on images with several same-category objects. Comparing these shows how performance depends on the data distribution, not just a single score.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

228 papers of our library cite it (of the 140884 whose bibliographies we read); 32 of them have a page here, 196 not yet.

- [Yi: Open Foundation Models by 01.AI](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-vl-versatile-vision-language/)
- [Qwen2.5-VL Technical Report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
- [Qwen3-VL Technical Report](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen3-vl-technical-report/)
- [UNITER: UNiversal Image-TExt Representation Learning](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/)
- [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/)
- [ShareGPT4V: Improving Large Multi-Modal Models with Better Captions](https://md.research.elamur.ai/free/en/papers/chen-2023-sharegpt4v-improving-large-multi-modal/)
- [Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic](https://md.research.elamur.ai/free/en/papers/chen-2023-shikra-unleashing-multimodal-llm-s/)
- [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/)
- [Unified Pragmatic Models for Generating and Following Instructions](https://md.research.elamur.ai/free/en/papers/fried-2018-unified-pragmatic-models-generating-following/)
- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/)
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- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://md.research.elamur.ai/free/en/papers/li-2019-visualbert-simple-performant-baseline-vision/)
- [Improved Baselines with Visual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://md.research.elamur.ai/free/en/papers/lu-2019-vilbert-pretraining-task-agnostic-visiolinguistic/)
- [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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- [A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
- [CogVLM: Visual Expert for Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/wang-2023-cogvlm-visual-expert-pretrained/)
- [Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution](https://md.research.elamur.ai/free/en/papers/wang-2024-qwen2-vl-enhancing-vision/)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/)
- [This One or That One? A Study on Accessibility via Demonstratives with Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-this-one-or-that-one/)
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- [Qwen2.5-Omni Technical Report](https://md.research.elamur.ai/free/en/papers/xu-2025-qwen2-5-omni-technical/)
- [Modeling Context in Referring Expressions](https://md.research.elamur.ai/free/en/papers/yu-2016-modeling-context-referring-expressions/)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://md.research.elamur.ai/free/en/papers/yue-2023-mmmu-massive-multi-discipline-multimodal/)
- [GPT-4V(ision) is a Generalist Web Agent, if Grounded](https://md.research.elamur.ai/free/en/papers/zheng-2024-gpt-4v-ision-generalist-web/)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2023-minigpt-4-enhancing-vision-language/)
- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://md.research.elamur.ai/free/en/papers/zhu-2025-internvl3-exploring-advanced-training/)

## Cite

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

```bibtex
@inproceedings{kazemzadeh2014referitgame,
  title = {{ReferItGame: Referring to Objects in Photographs of Natural Scenes}},
  author = {Kazemzadeh, Sahar and Ordonez, Vicente and Matten, Mark and Berg, Tamara},
  booktitle = {Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2014},
  doi = {10.3115/v1/d14-1086},
  url = {https://doi.org/10.3115/v1/d14-1086}
}
```

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