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<h1 align="center">💬 VLM-REG: Vision-Language Models Are Not Pragmatically Competent in Referring Expression Generation</h1>

<p align="center">
  📃 <a href="https://arxiv.org/abs/2504.16060" target="_blank">Paper</a> ｜🏠 <a href="https://vlm-reg.github.io" target="_blank">Homepage</a>
</p>



## Overview

Referring Expression Generation (REG)—the task of producing a concise and unambiguous description that allows a listener to identify a target object—lies at the heart of pragmatic communication in vision-language systems. However, existing benchmarks suffer from two major limitations:

1. **Data leakage in RefCOCO/RefCOCO+**, which raises concerns about evaluation contamination, especially for VLMs trained on MSCOCO.
2. **Lack of spoken data**, despite the fact that real-world referring is often **real-time** and **spontaneous**, unlike written language, which benefits from planning and revision.

To address these gaps, we introduce **RefOI**, a curated dataset built from the  [OpenImages V7](https://storage.googleapis.com/openimages/web/index.html)  Instance Segmentation validation set.

**Key features:**
- 1,485 real-world object instances, equally distributed across **COCO** (744) and **non-COCO** (741) classes.
- Includes **single presence** and **co-occurrence** images for each class.
- Each instance annotated with **3 written** and **2 spoken** human referring expressions.


Using RefOI, we evaluate several state-of-the-art VLMs and uncover **three tiers of pragmatic failure**:

- **Ambiguity**: Generated expressions often fail to uniquely identify the referent.
- **Redundancy**: Models include excessive or irrelevant details, violating principles of informativeness and efficiency.
- **Misalignment**: Model preferences diverge from human pragmatics, favoring visual complexity over minimal spatial cues.

![Overview](vlm-reg.png)


For token-level annotation of referring expressions, see the companion dataset [RefOI-TLHF](https://huggingface.co/datasets/Seed42Lab/RefOI-TLHF), which provides minimal span supervision for both human- and model-generated descriptions.


## Dataset Schema and Split

### Data Fields
Each entry in the dataset contains the following fields:

- `image`: The original image file.  
- `mask`: A binary segmentation mask isolating the target object.  
- `boxed_image`: The original image overlaid with a red bounding box highlighting the target object.  
- `box_xmin`, `box_xmax`, `box_ymin`, `box_ymax`: The normalized bounding‑box coordinates.  
- `is_coco`: A binary flag (1 for COCO-class, 0 for non‑COCO).  
- `label_name`: The object’s class label (e.g., “muffin,” “giraffe”).  
- `co_occurrence`: The number of same‑class instances in the image (1 = no distractors; >1 = multiple).  
- `written_descriptions`: Three human‑typed referring expressions.  
- `spoken_descriptions`: Two human‑spoken expressions (transcribed and optionally corrected by annotators).

### Dataset Split
- `single_presence` (`co_occurrence = 1`):  
  Only one object of the target class appears (no same‑class distractors in the image).

- `co_occurrence` (`co_occurrence > 1`):  
  Multiple objects of the same class appear in the image, introducing potential referential ambiguity.


## Usage

```python
from datasets import load_dataset

# only one object of the class
ds_single = load_dataset("Seed42Lab/RefOI", split="single_presence")
# multiple objects of the class
ds_multi = load_dataset("Seed42Lab/RefOI", split="co_occurrence")

print(ds_single[0])
print(ds_multi[0])
```


## Experiments

We compare multiple models across standard metrics, listener-based accuracy, and human judgment. Humans outperform all models by large margins (e.g., >90% vs. ~50%).
Automatic metrics such as BLEU and CIDEr show poor correlation with human judgment, frequently ranking verbose models higher. 
Even listener-based scores (REC) fail to consistently match human preferences, indicating that existing metrics do not capture pragmatic competence effectively.

| Model     | Instr. | BLEU-1    | BLEU-4   | ROUGE-1   | ROUGE-L   | METEOR    | CIDEr     | SPICE     | BERT      | CLIP      | REC       | Human     | Irrel%    |
| --------- | ------ | --------- | -------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- | --------- |
| LLaVA-7B  | Dft.   | 13.27     | 1.60     | 18.09     | 16.30     | 19.29     | 2.10      | 10.50     | 85.51     | 79.02     | 32.41     | 39.46     | 87.30     |
|           | Brf.   | 28.74     | 6.05     | **36.46** | 35.50     | 19.15     | 10.80     | 24.59     | 89.02     | 70.72     | 25.51     | 30.57     | 41.95     |
| LLaVA-13B | Dft.   | 8.17      | 1.07     | 11.98     | 10.94     | 16.89     | 0.77      | 7.92      | 84.61     | 79.85     | 30.13     | 46.40     | 91.85     |
|           | Brf.   | 28.96     | 5.81     | 36.44     | **35.64** | 20.13     | 8.14      | 21.63     | 88.42     | 72.99     | 28.92     | 32.53     | 49.65     |
| LLaVA-34B | Dft.   | 6.29      | 0.78     | 9.82      | 9.11      | 16.15     | 0.07      | 7.61      | 84.39     | 79.86     | 33.42     | 46.53     | 92.90     |
|           | Brf.   | 28.55     | 6.38     | 32.99     | 31.67     | 20.48     | 9.60      | 16.50     | 88.50     | 74.95     | 35.24     | 36.77     | 56.11     |
| XComposer | Dft.   | 5.25      | 0.65     | 8.38      | 7.81      | 14.58     | 3.10      | 6.37      | 84.11     | 79.86     | 38.06     | 52.19     | 92.81     |
|           | Brf.   | 13.59     | 2.17     | 17.77     | 16.69     | 19.95     | 5.52      | 10.63     | 85.52     | 79.66     | 38.47     | 51.65     | 80.36     |
| MiniCPM-V | Dft.   | 6.38      | 0.67     | 9.86      | 8.78      | 15.28     | 0.05      | 6.30      | 84.29     | 80.38     | 37.93     | 45.12     | 92.97     |
|           | Brf.   | 16.03     | 3.15     | 19.56     | 18.19     | 18.77     | 6.36      | 11.16     | 86.29     | 78.55     | 35.04     | 45.79     | 72.87     |
| GLaMM     | Dft.   | 15.01     | 3.32     | 16.69     | 16.29     | 11.49     | 9.08      | 3.90      | 86.42     | 58.26     | 5.78      | 3.84      | 74.68     |
|           | Brf.   | 18.46     | 4.45     | 20.92     | 20.46     | 14.18     | 10.48     | 4.44      | 86.65     | 58.60     | 5.72      | 4.85      | 70.52     |
| CogVLM    | Dft.   | 31.13     | **8.70** | 33.89     | 32.32     | 23.50     | **41.62** | 24.09     | 89.78     | 66.54     | 33.29     | 26.67     | **26.39** |
|           | Brf.   | **31.39** | 8.69     | 34.70     | 32.94     | **24.87** | 41.41     | **24.74** | **90.00** | 69.15     | 38.80     | 33.53     | 29.88     |
| GPT-4o    | Dft.   | 7.47      | 0.85     | 11.61     | 10.43     | 17.39     | 0.03      | 7.21      | 84.57     | **80.81** | **41.29** | **59.80** | 89.81     |
|           | Brf.   | 25.30     | 5.78     | 28.76     | 27.36     | 19.02     | 8.17      | 15.31     | 88.11     | 76.58     | 40.08     | 51.72     | 52.75     |
| Human     | Spk.   | 66.18     | 22.58    | 70.15     | 66.45     | 48.28     | 112.04    | 42.35     | 93.89     | 71.60     | 64.56     | 92.20     | 9.15      |
|           | Wrt.   | -         | -        | -         | -         | -         | -         | -         | -         | 70.43     | 63.69     | 89.29     | 7.29      |

Model performance under different **Instr.** (Instruction) settings: **Dft.** (Default) prompt and **Brf.** (Brief) prompt. All model predictions are evaluated against Human **Wrt.** (Written) results as the reference texts. We also compute Human **Spk.** (Spoken) data in comparison with human-written data. **Irrel%** refers to the percentage of irrelevant words in the referring expression of the examples evaluated as successful.



## Recommended Use of Our Dataset

The `RefOI` dataset is designed for fine-grained REG/REC analysis. It distinguishes between **COCO** and **non-COCO classes**, and between scenes with **single presence vs. co-occurrence** of the same class. 
We encourage users to leverage these distinctions for deeper insights and invite community contributions to expand non-COCO annotations.


## Citation
If you find our dataset helpful, please cite our work:
```bibtex
@misc{ma2025visionlanguagemodelspragmaticallycompetent,
      title={Vision-Language Models Are Not Pragmatically Competent in Referring Expression Generation}, 
      author={Ziqiao Ma and Jing Ding and Xuejun Zhang and Dezhi Luo and Jiahe Ding and Sihan Xu and Yuchen Huang and Run Peng and Joyce Chai},
      year={2025},
      eprint={2504.16060},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2504.16060}, 
}
```